ICAART 2010 Abstracts

Area 1 - Artificial Intelligence
Full Papers Short Papers
Area 2 - Agents
Full Papers Short Papers

Area 1 - Artificial Intelligence

Full Papers
Paper Nr: 52
Title:

Finding Distance-Based Outliers in Subspaces through both Positive and Negative Examples
Authors:

Fabio Fassetti and Fabrizio Angiulli
Abstract: In this work an example-based outlier detection method exploiting both positive (that is, outlier) and negative (that is, inlier) examples in order to guide the search for anomalies in an unlabelled data set, is introduced. The key idea of the method is to find the subspace where positive examples mostly exhibit their outlierness while at the same time negative examples mostly exhibit their inlierness. The degree to which an example is an outlier is measured by means of well-known unsupervised outlier scores evaluated on the collection of unlabelled data. A subspace discovery algorithm is designed, which searches for the most discriminating subspace. Experimental results show that the method is able to detect a near optimal solution, and that the method is promising from the point of view of the knowledge mined.
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Paper Nr: 66
Title:

Towards a discourse level coherence structure
Authors:

Wai Yeap and Parma Nand
Abstract: In this paper we argue that coherence relations between discourse units are ultimately based on mentioned discourse entities embedded in the units participating in the relation. Coherence relations as discussed in most literature ((Mann and Thompson, 1988), (Hobbs, 1985), (Grosz and Sidner, 1986) inter alia) are defined between text segments, where a text segment could range from a single utterance to the whole discourse. We show that these coherence relations are formed either directly or indirectly between embedded discourse entities. Other semantic entities might be derived via inference/s based on the mentioned entities and the complexity of these inferences determines some of the types of relations defined in literature. Hence, the coherence relations as defined by (Mann and Thompson, 1988), (Hobbs, 1985) inter alia, existing between text units is essentially an abstraction of these fundamental relations formed between embedded entities. We argue that any representation of discourse coherence structure should entail representation of information down to the resolution level of these embedded entities in order for such structures to be useful for automated language processing tasks. We also show that the commonly accepted tree structure ((Hobbs, 1985),(Marcu, 1996) inter alia) is not sufficient to represent discourse relations to such a resolution level, and propose a semi constrained directed graph as the alternative.
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Paper Nr: 85
Title:

XQUAKE: AN XQUERY-LIKE LANGUAGE FOR MINING XML DATA
Authors:

Andrea Romei and Franco Turini
Abstract: The rapid growth of semi-structured sources raises the need of designing and implementing environments for knowledge discovery out of XML data. This paper presents an Inductive Database System in which raw data, mining models and domain knowledge are represented as XML documents, stored inside XML native databases. In particular, we discuss our experiences in the design and development of XQuake, a mining query language that extends XQuery. Features of the language are an intuitive syntax, a good expressiveness and the capability of dealing uniformly with raw data, induced and background knowledge. The language is presented by means of examples and a sketch of its implementations and the evaluation of its performance is given.
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Paper Nr: 119
Title:

The (probabilistic) logical content of Cadiag2
Authors:

David Picado Muiño and David Picado
Abstract: Cadiag2 is a well-known rule-based expert system that aims at providing support for medical diagnose in internal medicine. Cadiag2 consists of a knowledge base in the form of a set of if-then rules that relate medical entities, in this paper interpreted as conditional probabilistic statements, and an inference engine constructed upon methods of fuzzy set theory. The aim underlying this paper is the understanding of the inference in Cadiag2. To that purpose we provide a (probabilistic) logical formalisation of the inference of the system and check its adequacy with probability theory.
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Paper Nr: 138
Title:

A Generic Solution to Multi-Armed Bernoulli Bandit Problems Based on Random Sampling from Sibling Conjugate Priors
Authors:

Ole-Christoffer Granmo, Thomas Norheim, John Oommen and Terje Bradland
Abstract: The Multi-Armed Bernoulli Bandit (MABB) problem is a classical optimization problem where an agent sequentially pulls one of multiple arms attached to a gambling machine, with each pull resulting in a random reward. The reward distributions are unknown, and thus, one must balance between exploiting existing knowledge about the arms, and obtaining new information. Although poised in an abstract framework, the applications of the MABB are numerous. On the other hand, while Bayesian methods are generally computationally intractable, they have been shown to provide a standard for optimal decision making. This paper proposes a novel MABB solution scheme that is inherently Bayesian in nature, and which yet avoids the computational intractability by relying simply on updating the hyper-parameters of the sibling conjugate distributions, and on simultaneously sampling randomly from the respective posteriors. Although, in principle, our solution is generic, to be concise, we present here the strategy for Bernoulli distributed rewards. Extensive experiments demonstrate that our scheme outperforms recently proposed bandit playing algorithms. We thus believe that our methodology opens avenues for obtaining improved novel solutions.
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Paper Nr: 162
Title:

On Using Simulation and Stochastic Learning for Pattern Recognition when Training Data is Unavailable:The Case of Disease Outbreak
Authors:

Dragos Calitoiu and John Oommen
Abstract: Pattern Recognition (PR) involves two phases, a Training phase and a Testing Phase. The problems associated with training a classifier when the number of training samples is small are well recorded. Typically, the matrices involved are ill-conditioned and the estimates of the probability distributions are very inaccurate, leading to a very poor classification system. In this paper, we report what we believe are the pioneering results for designing a PR system when there are absolutely no training samples. In such a scenario, we show how we can use a model of the underlying phenomenon and combine it with the principles of simulation and stochastic learning to design a very good classifier. By way of example, we consider the case of disease outbreak. We consider the problem of Learning the Contagion Parameter (LCP) in a black box model involving healthy, sick and contagious individuals. The model that we use is based on a well-established scientific model approximated for this preliminary study. The individuals move within a discretized grid, randomly, and we assume that the model is characterized by certain fixed parameters, some of which are known, while others are unknown. The parameter of interest in this report involves the probability with which an infected person will transmit the disease to a healthy person. The proposed solution utilizes the theory of Stochastic Point Location (SPL), and in the current study we assume that the Oracle is a stochastic Teacher. The problem then reduced to a PR or classification problem in which the SPL is first subjected to a training phase, the outcome of which is used for the testing phase. The results obtained are very impressive, and to the best of our knowledge, pioneering.
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Paper Nr: 191
Title:

AdaBoost based Door Detection for mobile robots
Authors:

Michael Blaich, Oliver Bittel and Jens Hensler
Abstract: Doors are important landmarks for robot selflocalization and navigation in indoor environments. Existing algorithms for door detection are often limited to restricted environments. They do not consider the large intra-class variability of doors. In this paper we present a camera- and laser-based approach which allows finding more than 82% of all doors with a false positive rate less than 3% in static test sets. By using different door perspectives from a moving robot, we detect more than 90% of doors with a very low false detection rate.
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Paper Nr: 224
Title:

SPCD - SPATIAL COLOR DESTRIBUTION DESCRIPTOR. A FUZZY RULE BASED COMPACT COMPOSITE DESCRIPTOR APPROPRIATE FOR HAND DRAWN COLOR SKETCHES RETRIEVAL
Authors:

Savvas Chatzichristofis, Yiannis Boutalis and Mathias Lux
Abstract: In this paper, a new low level feature suitable for Hand Drawn Color Sketches retrieval is presented. The proposed feature structure combines color and spatial color distribution information. The combination of these two features in one vector classifies the proposed descriptor to the family of Composite Descriptors. In order to extract the color information, a fuzzy system is being used, which is mapping the number of colors that are included in the image into a custom palette of 8 colors. The way by which the vector of the proposed descriptor is being formed, describes the color spatial information contained in images. To be applicable in the design of large image databases, the proposed descriptor is compact, requiring only 48 bytes per image. Experiments demonstrate the effectiveness of the proposed technique.
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Paper Nr: 233
Title:

A Cautious Approach to Generalization in Reinforcement Learning
Authors:

Raphael Fonteneau, Louis Wehenkel, Damien Ernst and Susan Murphy
Abstract: In the context of a deterministic Lipschitz continuous environment over continuous state spaces, finite action spaces, and a finite optimization horizon, we propose an algorithm of polynomial complexity which exploits weak prior knowledge about its environment for computing from a given sample of trajectories and for a given initial state a sequence of actions. The proposed Viterbi-like algorithm maximizes a recently proposed lower bound on the return depending on the initial state, and uses to this end prior knowledge about the environment provided in the form of upper bounds on its Lipschitz constants. It thereby avoids, in way depending on the initial state and on the the prior knowledge, those regions of the state space where the sample is too sparse to make safe generalizations. Our experiments show that it can lead to more cautious policies than algorithms combining dynamic programming with function approximators. We give also a condition on the sample sparsity ensuring that, for a given initial state, the proposed algorithm produces an optimal sequence of actions in open-loop.
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Paper Nr: 243
Title:

A GENERAL DIALOGUE MANAGEMENT MODEL FOR DYNAMIC-DOMAIN EXPERT SYSTEMS WITH NATURAL LANGUAGE INTERFACES
Authors:

Justyna Walkowska and Justyna Walkowska
Abstract: This paper describes a dialogue management model for a dynamic-domain multi-agent expert system with natural language competence. The solutions presented in this paper have been derived in the design and implementation process of Polint-112-SMS, an expert system to be used by security officers overseeing public events (e.g. concerts, football games). This paper presents a modular system architecture and explains the dialogue-oriented features of the modules. The presented problems and solutions include: unification of data obtained from different users, detecting and solving contradictions, pairing questions and answers in an asynchronous mode of communication, deciding when and how to contact the users to obtain more data. The model has been applied in practise and a number of tests have been performed, the results of which are also summarized herein.
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Paper Nr: 276
Title:

SELECTING GENES FROM GENE EXPRESSION DATA BY USING AN ENHANCEMENT OF BINARY PARTICLE SWARM OPTIMIZATION FOR CANCER CLASSIFICATION
Authors:

Mohd S. Mohamad, Sigeru Omatu, Michifumi Yoshioka and Safaai Deris
Abstract: In order to select a small subset of informative genes from gene expression data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an enhancement of binary particle swarm optimization to select a small subset of informative genes that is relevant for classifying cancer samples more accurately. In this proposed method, three approaches have been introduced to increase the probability of bits in particle’s positions to be zero. By performing experiments on three different gene expression data sets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.
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Paper Nr: 278
Title:

EpiAL - An Epigenetic Approach for an Artificial Life Model
Authors:

Jorge Sousa and Ernesto Costa
Abstract: Neo-Darwinist concepts have always been questioned and, nowadays, one of the sources of debate is epigenetic theory. Epigenetics study the relation between phenotypes and their environment, and the way this relation can regulate the genetic expression, while producing traits that can be inherited by offspring. This work presents an Artificial Life model designed with epigenetic concepts of regulation and inheritance. A platform was developed, in order to study the evolutionary significance of the epigenetic phenomena, both at individual and population levels. Differences were observed in the evolutionary behavior of populations, regarding the epigenetic variants. Agents without epigenetic structures display difficulties thriving in dynamic environments, while epigenetic based agents are able to achieve regulation. It is also possible to observe the persistence of acquired traits during evolution, despite the absence of the signal that induces those same traits.
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Paper Nr: 279
Title:

A PROCESS FOR COTS-SELECTION AND MISMATCHES HANDLING A Goal-Driven Approach
Authors:

Yves Wautelet, Manuel Kolp and Sodany Kiv
Abstract: Organizations facing the difficulties and costs associated with the development of their information systems from scratch turn to use commercial off-the-shelf (COTS) products to build their systems. A crucial factor in the success of such project is to perform a good COTS decision-making process, a process that aims at defining the organizations’ requirements, evaluating existing products and selecting the one that best fits requirements. However, even the best-fitting product would not perfectly match requirements, this is referred to as COTS mismatches. These mismatches occur as a result of an excess or shortage of COTS capabilities. Many of these mismatches are resolved after the COTS selection. This paper presents a goal-driven agent-oriented approach for proceeding the COTS decision-making and analysing mismatches during and after the COTS selection. The methodology is overviewed and illustrated on a case study.
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Paper Nr: 286
Title:

CHANGING TOPICS OF DIALOGUE FOR NATURAL MIXED-INITIATIVE INTERACTION OF CONVERSATIONAL AGENT BASED ON HUMAN COGNITION AND MEMORY
Authors:

Sungsoo Lim, Keunhyun Oh and Sung-Bae Cho
Abstract: Mixed-initiative interaction (MII) plays an important role in conversation agent. In the former MII research, MII process only static conversation and cannot change the conversation topic dynamically by the system because the agent depends only on the working memory and predefined methodology. In this paper, we propose the mixed-initiative interaction based on human cognitive architecture and memory structure. Based on the global workspace theory, one of the cognitive architecture models, proposed method can change the topic of conversation dynamically according to the long term memory which contains past conversation. We represent the long term memory using semantic network which is a popular representation for storing knowledge in the field of cognitive science, and retrieve the semantic network according to the spreading activation theory which has been proven to be efficient for inferencing in semantic networks. Through some dialogue examples, we show the usability of the proposed method.
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Paper Nr: 287
Title:

A Conceptual Study of Model Selection in Classification: Multiple Local Models vs One Global Model
Authors:

Ricardo Vilalta, Francisco Ocegueda-Hernandez and Chaitanya Bagaria
Abstract: A key concept in model selection is to understand how model complexity can be modified to improve in generalization performance. One design alternative is to increase model complexity on a single global model (by increasing the degree of a polynomial function); another alternative is to combine multiple local models into a composite model. We provide a conceptual study that compares these two alternatives. Following the Structural Risk Minimization framework, we derive bounds for the maximum number of local models or folds below which the composite model remains at an advantage with respect to the single global model. Our results can be instrumental in the design of learning algorithms displaying better control over model complexity.
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Paper Nr: 296
Title:

A Real-Time Hybrid Method for People counting system in a multi-state environment
Authors:

Ali Rezaee, Ali Rezaee, Hojjat Bagherzadeh, Vahid Vahid and Hamid Abrishami
Abstract: Detecting and tracking people in real-time in complicated and crowded scenes is a challenging problem. This paper presents a multi-cue methodology to detect and track pedestrians in real-time in the entrance gates using stationary CCD cameras. The proposed approach is the combination of two main algorithms, the detecting and tracking for solitude situations and an estimation process for too crowded scenes. In the former method, the detection component includes finding local maximums in foreground mask of Gaussian-Mixture and Ω-shaped objects in the edge map by trained PCA. And the tracking engine employs a Dynamic VCM with automated criteria based on the shape and size of detected human shaped entities. This new approach has several advantages. First, it uses a well-defined and robust feature space which includes polar and angular data. Furthermore due to its fast method to find human shaped objects in the scene, it’s intrinsically suitable for real-time purposes. In addition, this approach verifies human formed objects based on PCA algorithm, which makes it robust in decreasing false positive cases. This novel approach has been implemented in a sacred place and the experimental results demonstrated the system’s robustness under many difficult situations such as partial or full occlusions of pedestrians.
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Paper Nr: 333
Title:

Combining Runtime Diagnosis and AI-Planning in a Mobile Autonomous Robot to Achieve a Graceful Degradation after Software Failures
Authors:

Joerg Weber and Franz Wotawa
Abstract: Our past work deals with model-based runtime diagnosis in the software system of a mobile autonomous robot. Unfortunately, as an automated repair of failed software components at runtime is hardly possible, it may happen that failed components must be removed from the control system. In this case, those capabilities of the control system which depend on the removed components are lost. This paper focuses on the necessary adaptions of the high-level decision making in order to achieve a graceful degradation. Assuming that those decisions are made by an AI-planning system, we propose extensions which enable such a system to generate only plans which can be executed and monitored despite the lost capabilities. Among others, we propose an abstract model of software capabilities, and we show how to dynamically determine those capabilities which are required for monitoring a plan.
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Paper Nr: 337
Title:

A GENETIC ALGORITHM FOR CLUSTERING TEMPORAL DATA - Application to the Detection of Stress from ECG Signals
Authors:

Ana Fred and Liliana S. Medina
Abstract: Electrocardiography signals are typically analyzed for medical diagnosis of pathologies and are relatively unexplored as physiological behavioral manifestations. In this work we propose to analyze these signals with the intent of assessing the existence of significant changes of their features related to stress occurring in the performance of a computer-based cognitive task. Given the exploratory nature of this analysis, usage of unsupervised learning techniques is naturally adequate for our purposes. We propose a work methodology based on unsupervised automatic methods, namely clustering algorithms and clustering ensemble methods, as well as on evolutionary algorithms. The implemented automatic methods are the result of the adaptation of existing clustering techniques, including evolutionary computation, with the goal of detecting patterns by analysis of data with continuous temporal evolution. We propose a genetic algorithm for the specific task of assessing the continuous evolution and the separability of the stress states. The obtained results show the existence of differentiated states in the data sets that represent the ECG signals, thus confirming the adequacy and validity of the proposed methodology in the context of the exploration of these electrophysiological signals for emotional states detection.
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Short Papers
Paper Nr: 27
Title:

Prediction of surface roughness in turning using orthogonal matrix experiment and neural networks

Authors:

George Petropoulos, Vassilis Iakovakis, John Kechagias, Stergios MAROPOULOS, Stefanos Karagiannis, Vassilis Iakovakis and George Petropoulos
Abstract: A neural network modeling approach is presented for the prediction of surface texture parameters during turning of a copper alloy (GC-CuSn12). Test specimens in the form of near-to-net-shape bars and a titanium nitride coated cemented carbide (T30) cutting tool were used. The independent variables considered were the cutting speed, feed rate, cutting depth and tool nose radius. The corresponding surface texture parameters that have been studied are the Ra, Rq, and Rt. A feed forward back propagation neural network was developed using experimental data which were conducted on a CNC lathe according to the principles of Taguchi design of experiments method. It was found that NN approach can be applied in an easy way on designed experiments and predictions can be achieved, fast and quite accurate. The developed NN is constrained by the experimental region in which the designed experiment is conducted. Thus, it is very important to select parameters’ levels as well as the limits of the experimental region and the structure of the orthogonal experiment. This methodology could be easily applied to different materials and initial conditions for optimization of other manufacturing processes.
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Paper Nr: 33
Title:

INVERSION FUNCTION OF MDS FOR SENTENCES ANALYSIS
Authors:

Erqing Xu
Abstract: Traditional sentence analysis refers to finding the sentence structure for a given sentence. A question different from this is: given a sentence Curry-Horwad isomorphic with a type, can we establish the proof tree representing the sentence? Therefore, this paper combines the extensional Kripke interpretation and MDS (Minimalist Deductive System); derives the Kripke model of MDS; provides the applicable inversion function such that we are able to obtain the proof tree of typed -terms which represents sentence structure; and demonstrates that the product-free proof trees obtained with inversion function of MDS enjoy the property of Church-Rosser equality. Application examples demonstrate that our work is valid. The main difference between our work and traditional sentence analysis approach is that the objects of analysis are different. The object of our work is: Kripke model of MDS and type of sentence satisfied by assignment. But the object of traditional sentence analysis approach is sentence. This paper enlarges the range of application of sentence analysis, improves sentence analysis approach, enhances natural language understanding, and thus is meaningful. Our work has not been seen in literature.
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Paper Nr: 44
Title:

Efficient image reduction for fast intelligible classification
Authors:

Marc Joliveau
Abstract: In the past decades, many domains collected great amounts of data, particularly multimedia files, and stored them in large databases. Therefore, area such as similarity search for image learning have received much attention in the recent years. This paper presents an innovative way to strongly reduce dimension and keep relations between components of an image data set. Our method is validated on the Mnist learning database containing 70000 pictures of handwritten digits. Results demonstrate that the proposed approach is very efficient. It allows to accurately classify, learn, and identify digits using very short computation time in comparison with those obtained with original full-size images.
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Paper Nr: 47
Title:

A CONVERSATIONAL EXPERT SYSTEM SUPPORTING BULLYING AND HARASSMENT POLICIES
Authors:

Annabel Latham, Annabel Latham, ZUHAIR BANDAR and Keeley Crockett
Abstract: In the UK, several laws and regulations exist to protect employees from harassment within the workplace. Organisations operating in the UK normally implement this legislation by creating comprehensive and carefully-worded bullying and harassment policies and procedures to cover the key aspects of each regulation. In large organisations, such policies often result in a high support cost, including specialist training for the management team and human resources counsellors. This paper presents a novel conversational expert system which supports bullying and harassment policies in large organisations. Information about the bullying and harassment policies and their application within organisations was acquired using knowledge engineering techniques. A knowledge tree is used to represent the knowledge intuitively, and a dynamic graphical user interface (GUI) is proposed to enable the knowledge to be traversed graphically. Adam is a conversational agent allowing users to type in questions in natural language at any point and receive a simple and direct answer. An independent evaluation of the system has given promising results.
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Paper Nr: 53
Title:

Detection of Discriminating Rules
Authors:

Domenico Trimboli, Fabio Fassetti, Fabrizio Angiulli, Luigi Palopoli and Domenico Trimboli
Abstract: Assume a population partitioned in two subpopulations, e.g. a set of normal individuals and a set of abnormal individuals, is given. Assume, moreover, that we look for a characterization of the reasons discriminating one subpopulation from the other. In this paper, we provide a technique by which such an evidence can be mined, by introducing the notion of discriminating rule, that is a kind of logical implication which is much more valid in one of the two subpopulations than in the other one. In order to avoid mining a potentially huge number of (not necessarily interesting) rule, we define a preference relationship among rules and exploit a suitable graph encoding in order to single out the most interesting ones, which we call outstanding rules. We provide an algorithm for detecting the outstanding discriminating rules and present experimental results obtained by applying the technique in several scenarios.
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Paper Nr: 64
Title:

MULTI-AGENT VOTING FOR CONFLICT RESOLUTION: A FUZZY APPROACH

Authors:

Miklos Nagy, Miklos Nagy and Maria Vargas-Vera
Abstract: Software agents that interpret the possible meaning of Semantic Web data differently should be able to resolve their differences i.e. resolve conflicts effectively. One typical use case is ontology mapping where different agents using different similarity measures create beliefs in the assessed similarities, which needs to be combined into a more coherent state. The combination of these contradicting beliefs can easily worsen the mapping precision and recall, which leads to poor performance of any ontology mapping algorithm. In these scenarios agents, which use different similarities and combine them into a more reliable and coherent view can easily become unreliable when these contradictions are not managed effectively between the different agents. In this paper we propose a solution based on the fuzzy voting model for managing such situations by introducing trust and voting between software agents that resolve contradicting beliefs in the assessed similarities.
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Paper Nr: 69
Title:

POSSIBILISTIC ACTIVITY RECOGNITION
Authors:

Patrice Roy, Abdenour Bouzouane, Sylvain Giroux and Bruno Bouchard
Abstract: The development towards ambient computing will stimulate research in many fields of artificial intelligence, such as activity recognition. To address this challenging issue, we present a formal activity recognition framework based on possibility theory, which is largely different from the majority of all recognition approaches proposed that are usually based on probability theory. To validate this novel alternative, we are developing an ambient agent for the cognitive assistance of an Alzheimer’s patient within a smart home, in order to identify the various ways of supporting him in carrying out his activities of daily living.
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Paper Nr: 71
Title:

B-CUBE MODEL IN AUTOMATED FUNCTIONAL DESIGN
Authors:

Vicente Chulvi and Rosario Vidal
Abstract: The present work proposes to use both the NIST’s functional basis and the B-Cube model in the development of a KBS that is capable of automating functional design. For this purpose this article shows with an example how the system will work using the FBS framework. The starting point is defended by the terms of NIST functional basis. The evolution from functions to structures, that is, to the solution, is achieved by means of B-Cube model in the behaviour layer.
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Paper Nr: 78
Title:

A Generic Approach For Sparse Path Problems
Authors:

Marc Pouly
Abstract: This paper shows how sparse path problems can be solved by tree-decomposition techniques. We analyse the properties of closure matrices and prove that they satisfy the axioms of a valuation algebra, which is known to be sufficient for the application of generic tree-decomposition methods. Given a sparse path problem where only a subset of queries are required, we continually compute path weights of smaller graph regions and deduce the total paths from these results. The decisive complexity factor is no more the total number of graph nodes but the induced treewidth of the path problem.
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Paper Nr: 81
Title:

Exploiting Similarity Information in Reinforcement Learning. Similarity Models for Multi-Armed Bandits and MDPs.
Authors:

Ronald Ortner
Abstract: This paper considers reinforcement learning problems with additional similarity information. We start with the simple setting of multi-armed bandits in which the learner knows for each arm its color, where it is assumed that arms of the same color have close mean rewards. An algorithm is presented that shows that this color information can be used to improve the dependency of online regret bounds on the number of arms. Further, we discuss to what extent this approach can be extended to the more general case of Markov decision processes. For the simplest case where the same color for actions means similar rewards and identical transition probabilities, an algorithm and a corresponding online regret bound are given. For the general case where transition probabilities of same-colored actions imply only close but not necessarily identical transition probabilities we give upper and lower bounds on the error by action aggregation with respect to the color information. These bounds also imply that the general case is far more difficult to handle.
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Paper Nr: 83
Title:

Learning and Prediction based on a Relational Hidden Markov Model
Authors:

Carsten Elfers and Thomas Wagner
Abstract: In this paper we show a novel method on how the well-established hidden markov model and the relational markov model can be combined to the relational hidden markov model to solve currently unrecognized challenging problems of the original models. Our presented methods allows for prediction on different granularity level depending on the validity of the underlying observations. We demonstrate the use of this new method based on a spatio-temporal qualitative representation and validate the approach in the RoboCupSoccer multiagent environment.
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Paper Nr: 94
Title:

DOCUMENTS REPRESENTATION BASED ON INDEPENDENT COMPRESSIBILITY FEATURE SPACE
Authors:

Nuo Zhang
Abstract: There are two well-known feature representation methods, bag-of-words and N-gram models, which have been widely used in natural language processing, text mining, and web document analysis. A novel Pattern Representation scheme using Data Compression (PRDC) has been proposed for data representation. The PRDC not only can process data of linguistic text, but also can process the other multimedia data effectively. Although PRDC provides better performance than the traditional methods in some situation, it still suffers the problem of dictionary selection and construction of feature space. In this study, we propose a method for PRDC to construct an independent compressibility space, and compare the proposed method to the two other representation methods and PRDC. The performance will be compared in terms of clustering ability. Experiment results will show that the proposed method can provide better performance than that of PRDC and the other two methods.
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Paper Nr: 100
Title:

ROBUST METHODS FOR ROBOT LOCALIZATION UNDER CHANGING ILLUMINATION CONDITIONS
Authors:

Lorenzo Fernández Rojo, Luis Payá, Oscar Reinoso, Arturo Gil Aparicio and Miguel Juliá Cristóbal
Abstract: The use of omnidirectional systems provides us with rich visual information that allows us to create appearance-based dense maps. This map can be composed of several panoramic images taken from different positions in the environment. When the map contains only visual information, it will depend heavily on the conditions of the environment lighting. Therefore we get different visual information depending on the time of day when the map is created, the state of artificial lighting in the environment, or any other circumstance that causes a change in the illumination of the scene. To obtain a robust map against changes in the illumination of the environment we apply different filters on the panoramic images. After that, we use some compression methods that allow us to reduce the amount of information stored. We have conducted a comprehensive experimentation to study which type of filter best adapts to changing lighting conditions.
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Paper Nr: 139
Title:

DETECTION OF DISTRIBUTED ATTACKS IN MOBILE AD-HOC NETWORKS USING SELF-ORGANIZING TEMPORAL NEURAL NETWORKS
Authors:

James Cannady
Abstract: Mobile ad hoc networks continue to be a difficult environment for effective intrusion detection. In an effort to achieve reliable distributed attack detection in a resource-efficient manner a self-organizing neural network-based intrusion detection system was developed. The approach, Distributed Self-organizing Intrusion Response (DISIR), enables real-time detection in a decentralized manner that demonstrates a distributed analysis functionality which facilitates the detection of complex attacks against MANETs. The results of the evaluation of the approach and a discussion of additional areas of research is presented.
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Paper Nr: 140
Title:

On Reducing Dimensionality of Dissimilarity Matrices for Optimizing DBC - An Experimental Comparison
Authors:

Sang-Woon Kim
Abstract: One problem of dissimilarity-based classifications (DBCs) is the high dimensionality of dissimilarity matrices. To address this problem, two kinds of solutions have been proposed in the literature: prototype selection (PS) based methods and dimensionality reduction (DR) based methods. The DR-based method consists of building the dissimilarity matrices using all the available training samples and subsequently applying some of the standard DR schemes. On the other hand, the PS-based method works by directly choosing a small set of representatives from the training samples. Although DR-based and PS-based methods have been explored separately by many researchers, not much analysis has been done on the study of comparing the two. Therefore, this paper aims to find a suitable method for optimizing DBCs by a comparative study. In the experiments, four DR and four PS methods are used to reduce the dimensionality of the dissimilarity matrices, and classification accuracies of the resultant DBCs trained with two real-life benchmark databases are analyzed. Our empirical evaluation on the two approaches demonstrates that the DR-based method can improve the classification accuracies more than the PS-based method. Especially, the experimental results show that the DR-based method is clearly more useful for nonparametric classifiers, but not for parametric ones.
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Paper Nr: 156
Title:

MULTIAGENT COORDINATION IN AD-HOC NETWORKS BASED ON COALITION FORMATION
Authors:

Usama Mir, Luciana Bezerra Arantes, Samir Aknine, Luciana Bezerra Arantes, Samir Aknine and Usama Mir
Abstract: This research investigates the problems of agent coordination when deployed in highly dynamic environments such as MANETs (Mobile Ad-hoc NETworks). Several difficulties arise in these infrastructures especially when the devices with limited resources are used. All the constraints of agents’ development must thus be reexamined in order to deal with such situations, especially due to the opportunistic mobility of nodes. In this paper, we address these original problems and we propose a new coordination mechanism through agent coalition formation. We present this mechanism and evaluate it on an application devoted for the assistance of hospital patients. Finally we delineate the results of the conducted experiments.
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Paper Nr: 177
Title:

AFFECTIVE BLOG ANALYZER What People Feel to
Authors:

Masato Tokuhisa, Jin'ichi Murakami and Satoru Ikehara
Abstract: This paper proposes an affective blog analyzer which can capture people's emotional targets. The existing affective analysis has some problems. For instance, polarity analysis or positive/negative classification for documents are developed, but emotional targets can not be extracted. Some investigations can capture customer's wanted/needed objects, but the knowledge is domain dependent. Therefore, it can not analyze people's everyday life. Against these problems, this paper uses a sentence pattern dictionary to analyze emotions. The dictionary covers Japanese fundamental 6,000 verbs and contains 14,800 patterns with emotional information for everyday life. This dictionary is available for analyzing the downloaded blog articles. After analyzing blogs, many keywords can be extracted as emotional targets. In order to filter and sort them for supporting blog analysts, two parameters are applied. One is Z-score in terms of the frequency of the target appearance, and another is probability of emotions. In the experiments, trendy and emotional targets were successfully extracted from 6-month-blogs. Thus, the effects of the patterns and parameters are confirmed.
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Paper Nr: 183
Title:

NLP AND ONTOLOGY MATCHING: A SUCCESSFUL COMBINATION FOR TRIALOGICAL LEARNING
Authors:

Angela Locoro, Anna M. Scapolla and Viviana Mascardi
Abstract: Trialogical Learning refers to those forms of learning where learners are collaboratively developing, transforming, or creating shared objects of activity in a systematic fashion. In order to be really productive, Trialogical Learning must rely on knowledge that co-evolves with social practices and that can be modified, in an automatic or semi-automatic way, according to the users’ emerging needs and practical innovations. These requirements raise problems related to knowledge evolution, content retrieval and classification, dynamic suggestion of relationships among knowledge objects. In this paper, we propose to exploit Natural Language Processing and Ontology Matching techniques for facing the problems above. The Knowledge Practice Environment of the KP-Lab project has been used as a test bed for demonstrating the feasibility of our approach.
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Paper Nr: 192
Title:

Complexity of stochastic branch and bound methods for belief tree search in Bayesian reinforcement learning
Authors:

Christos Dimitrakakis
Abstract: There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most problems of interest, the optimal solution involves planning in an infinitely large tree. However, it is possible to obtain stochastic lower and upper bounds on the value of each tree node. This enables us to use stochastic branch and bound algorithms to search the tree efficiently. This paper proposes some algorithms and examines their complexity in this setting.
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Paper Nr: 194
Title:

Improving the Performance of CODEQ using Quadratic Interpolation
Authors:

Mahamed H. Omran, Mahamed Omran and Ayed Salman
Abstract: CODEQ is a new, population-based meta-heuristic algorithm that is a hybrid of concepts from chaotic search, opposition-based learning, differential evolution and quantum mechanics. CODEQ has successfully been used to solve different types of problems (e.g. constrained, integer-programming, engineering) with excellent results. In this paper, a new mutated vector based on quadratic interpolation (QI) is incorporated into CODEQ. The proposed method is com-pared with the original CODEQ and a differential evolution variant the uses QI on eleven benchmark functions. The results show that using QI improves both the efficiency and effectiveness of CODEQ.
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Paper Nr: 195
Title:

Stochastic GPU-based Multithread Implementation of Multiple Back-Propagation
Authors:

Noel Lopes and Bernardete M. Ribeiro
Abstract: Graphics Processing Units (GPUs) have evolved into a highly parallel, multi-threaded, many-core processor with enormous computational power. The GPU is especially well suited to address pattern recognition problems that can be expressed as data-parallel computations. Thus it provides a viable alternative to the use of dedicated hardware in the neural network (NN) field, where the long training times have always been a major drawback. In this paper, we propose a GPU implementation of the online (stochastic) training mode of the Multiple Back-Propagation (MBP) algorithm and compare it with corresponding standalone CPU version and with the batch training mode GPU implementation. For a fair and unbiased comparison we run the experiments with benchmarks from machine learning and pattern recognition field and we show that the GPU performance excel the CPU results in particular for high complex problems.
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Paper Nr: 209
Title:

A New Representation and Planner for Computer Batch Job Scheduling, Execution Monitoring, Problem Diagnosis and Correction
Authors:

Tracey Lall and Tracey Lall
Abstract: Modern enterprise computer environments use commercial schedulers to run and monitor computer batch jobs and processes. Currently the job schedules must be manually designed to include diagnosis and error correction behaviours for failed jobs or failures must be handled by support staff at execution time, requiring them to be on call while these jobs run. Automating these manual tasks using planning techniques requires a compact representation of contingent plans, handling and monitoring of actions which have a variable duration, actions which are triggered by external events and planning for knowledge goals. Currently these features are not provided by any existing single planner. We present a novel plan representation which drawing on existing scheduler representations provides all these features in an integrated manner. A planner implementation using this representation with a new action logic is described along with key worked examples from the domain.
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Paper Nr: 210
Title:

Agent-based Information Customization System using CBR and Ontology

Authors:

Hyun J. Lee and Mye Sohn
Abstract: An Agent-based Information Customizing System is composed of a Case Generation Agent to transform unstructured documents to a structured form such as cases, and a Case Customization Agent to select the most similar case from the case-base and adjust the selected case depending on the user’s information requirements. The developed case contains features and their values, and each defined case that is based on the information can each has a different feature set. Thus, it is possible to represent the contained details in documents, and it is easier to find more appropriate information from the case pool. Two-step similarity calculation using features and values and domain ontology are applied to find an appropriate case. A case customization process is suggested to adjust the case. In our future work, the suggested system would be applied to traveller’s systems to integrate information and will prove its effectiveness.
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Paper Nr: 215
Title:

SHAPE PRIOR SEGMENTATION OF MEDICAL IMAGES USING PARTICLE SWARM OPTIMIZATION

Authors:

Ahmed Afifi, Norimichi Tsumura and Toshiya Nakaguchi
Abstract: The image segmentation is the first and essential process in many medical applications. This process is traditionally performed by radiologists or medical specialists to manually trace the objects on each image. In almost all of these applications, the medical specialists have to access a large number of images which is a tedious and a time consuming process. On the other hand, the automatic segmentation is still challenging because of low image contrast and ill-defined boundaries. In this work, we propose a fully automated medical image segmentation framework. In this framework, the segmentation process is constrained by two prior models; a shape prior model and a texture prior model. The shape prior model is constructed from a set of manually segmented images using the principle component analysis (PCA) while the wavelet packet decomposition is utilized to extract the texture features. The fisher linear discriminate algorithm is employed to build the texture prior model from the set of texture features and to perform a preliminary segmentation. Furthermore, the particle swarm optimization algorithm (PSO) is used to refine the preliminary segmentation according to the shape prior model. In this work, we tested the proposed technique for the segmentation of the liver from abdominal CT scans and the obtained results show the efficiency of the proposed technique to accurately delineate the desired objects.
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Paper Nr: 217
Title:

Integrating Reasoning About Actions and Bayesian Networks
Authors:

Yves Martin and Michael Thielscher
Abstract: According to the paradigm of Cognitive Robotics (Reiter, 2001a), intelligent, autonomous agents interacting with an incompletely known world need to reason logically about the effects of their actions and sensor information they acquire over time. In realistic settings, both the effect of actions and sensor data are subject to errors. A cognitive agent can cope with these uncertainties by maintaining probabilistic beliefs about the state of world. In this paper, we show a formalism to represent probabilistic beliefs about states of the world and how these beliefs change in the course of actions. Additionally, we propose an extension to a logic programming framework, the agent programming language FLUX, to actually infer this probabilistic knowledge for agents. Using associated Bayesian networks allows the agents to maintain a single and compact probabilistic knowledge state throughout the execution of an action sequence.
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Paper Nr: 228
Title:

A knowledge-based system for the validation of the deployment of software units
Authors:

Laura Díaz-Casillas, Mercedes Garijo and Francisco Javier Blanco
Abstract: Today, many business applications are developed following SOA principles. One of the activities required for their implementation is deployment, a complex process that usually is done by hand, being necessary to develop new tools to facilitate it. This article presents a knowledge-based system that validates the deployment of software units on a particular environment, before executing them. The system is based on an information model and has been implemented with Drools 5.0 and as an OSGi bundle to be integrated into a deployment and configuration architecture.
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Paper Nr: 234
Title:

PREDICTING USER ACTIVITIES IN THE SEQUENCE OF MOBILE CONTEXT FOR AMBIENT INTELLIGENCE ENVIRONMENT USING DYNAMIC BAYESIAN NETWORK
Authors:

Han-Saem Park and Sung-Bae Cho
Abstract: Recently, mobile devices became essential mediums in order to implement ambient intelligence. Since people can always keep these mobile devices, it is easy for them to collect diverse user information. Therefore, many research groups have attempted to provide useful services based on this ubiquitous information. This paper proposes a method to predict user activity in the sequence of mobile context. In order to conduct accurate prediction of activity among various patterns, we have considered user activity, place, time and day of week as mobile context. We have used dynamic Bayesian network to model the user activity patterns with this context, and learned the model of each individual to obtain better model. For experiments, we have collected the mobile logs of undergraduate students, and confirmed that the proposed method produced good performance.
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Paper Nr: 238
Title:

Research on the Bayesian Learning Model for Selecting Arguments on Argumentation-based Negotiation of Agent
Authors:

Guorui Jiang, Xiaoyu Hu and Xiuzhen Feng
Abstract: In the Argumentation-based negotiation of agent, it is important to enhance the agent’s ability according to the environment, which would improve the argumentation efficiency significantly. Introducing Bayesian learning model to select arguments in Argumentation-based negotiation, the agent is able to learn and adjust itself according to a dynamic environment. This helps in making more rational and scientific choice for advancing efficiency of argumentation, when it is facing a variety of options for sending arguments. Finally, an example was presented for proving the rationality and validity of the model.
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Paper Nr: 252
Title:

A Niched Pareto Genetic Algorithm For Multiple Sequence Alignment Optimization
Authors:

Fernando Silva, Miguel A. Vega-Rodríguez, Juan M. Sánchez-Pérez and Juan A. Gómez-Pulido
Abstract: The alignment of molecular sequences is a recurring task in bioinformatics, but it is not a trivial problem. The size and complexity of the search space involved difficult the task of finding the optimal alignment of a set of sequences. Due to its adaptive capacity in large and complex spaces, Genetic Algorithms emerge as good candidates for this problem. Although they are often used in single objective domains, its use in multidimensional problems allows finding a set of solutions which provide the best possible optimization of the objectives – the Pareto front. Niching methods, such as sharing, distribute these solutions in space, maximizing their diversity along the front. We present a niched Pareto Genetic Algorithm for sequence alignment which we have tested with six BAliBASE alignments, taking conclusions regarding population evolution and quality of the final results. Whereas methods for finding the best alignment are mathematical, not biological, having a set of solutions which facilitate experts’ choice, is a possibility to consider.
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Paper Nr: 277
Title:

DIALOGUE-BASED MANAGEMENT OF USER FEEDBACK IN AN AUTONOMOUS PREFERENCE LEARNING SYSTEM
Authors:

Juan M. Lucas-Cuesta, Javier Ferreiros, Michael F. Mctear, Juan C. Augusto and Asier Aztiria
Abstract: We present an enhanced method for user feedback in an autonomous learning system that includes a spoken dialogue system to manage the interactions between the users and the system. By means of a rule-based natural language understanding module and a state-based dialogue manager we allow the users to update the preferences learnt by the system from the data obtained from different sensors. The design of the dialogue together with the storage of context information (the previous dialogue turns and the current state of the dialogue) ensures highly natural interactions, reducing the number of dialogue turns and making it possible to use complex linguistic constructions instead of isolated commands.
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Paper Nr: 294
Title:

Handling Dynamic Multiobjective Problems with Particle Swarm Optimization
Authors:

Alan D. Manríquez, Gregorio Toscano-Pulido and José G. Ramírez-Torres
Abstract: In this paper the hyperplane distribution and Pareto dominance were incorporated into a particle swarm optimization algorithm in order to allow it to handle dynamic multiobjective problems. When a change in a dynamic multiobjectve function is detected, the proposed algorithm reinitializes (in different ways) the PSO's velocity parameter and the archive where the non-dominated solutions are beeing stored such that the algorithm can follow the dynamic Pareto front. The proposed approach is validated using two dynamic multiobjective test functions and an standard metric taken from the specialized literature. Results indicate that the proposed approach is highly competitive which can be considered as a viable alternative in order to solve dynamic multiobjective optimization problems.
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Paper Nr: 298
Title:

An Online Speaker Tracking System for Ambient Intelligence Environments
Authors:

Luis J. Rodríguez-Fuentes, Germán Bordel, Juan Pedro Uribe, Maider Zamalloa and Mikel Penagarikano
Abstract: Ambient intelligence is an interdisciplinary paradigm which envisages smart spaces that provide services and adapt transparently to the user. As the most natural interface for human interaction, speech can be exploited for adaptation purposes in such scenarios. Low latency is required, since adaptation must be continuous. Most speaker tracking approaches found in the literature work offline, fully processing pre-recorded audio files by a two-stage procedure: (1) performing acoustic segmentation and (2) assigning each segment a speaker label. In this work a real-time low-latency speaker tracking system is presented, which deals with continuous audio streams. Experimental results are reported on the AMI Corpus of meeting conversations, revealing the effectiveness of the proposed approach when compared to an offline speaker tracking system developed for reference.
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Paper Nr: 299
Title:

A Non-Parameterised Hierarchical Pole Based Clustering Algorithm (HPoBC)
Authors:

Amparo Albalate, Steffen Rhinow and David Suendermann
Abstract: In this paper we propose a hierarchical, divisive, clustering algorithm, called Hierarchical Pole Based Clustering (HPoBC), which is able to find the clusters in a data set without any user input parameter such as the number of clusters k. The algorithm is based on the Pole Based Overlapping Clustering (PoBOC). Initially, the top hierarchy level is composed by the set of clusters discovered by the PoBOC algorithm on the dataset. Then, each single cluster is again analysed using a combination of PoBOC and cluster validity methods (silhouettes) in order to search for new possible subclusters. This process is recursively repeated on each newly retrieved cluster until the silhouette score suggests to stop any further partitioning of the cluster. The HPoBC algorithm has been compared to the original PoBOC as well as other classical hierarchical approaches on five bidimensional, synthetic data sets, using three cluster evaluation metrics.
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Paper Nr: 302
Title:

INTERPRETING STRUCTURES IN MAN-MADE SCENES; Combining Low-Level and High-Level Structure Sources
Authors:

Kasim Terzić, Jan Šochman, Kasim Terzic, Lothar Hotz and Jan Sochman
Abstract: Recognizing structure is an important aspect of interpreting many computer vision domains. Structure can manifest itself both visually, in terms of repeated low-level phenomena, and conceptually, in terms of a high- level compositional hierarchy. In this paper, we demonstrate an approach for combining a low-level repetitive structure detector with a logical high-level interpretation system. We evaluate the performance on a set of images from the building facade domain.
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Paper Nr: 305
Title:

ROSE -- An Intelligent Mobile Assistant
Authors:

Bjoern Zenker, Bjoern Zenker and Bernd Ludwig
Abstract: In this paper, we describe ROSE (Routing Service), an application for mobile phones, which suggests the user events and locations and guides him to them, using the public transport system. It determines the best possible transport link and accompanies passengers throughout their entire journey. Further, it reacts in real time to delays in the public transport system and calculates alternative routes when necessary. For route planning in this context, we will propose a h-optimal algorithm for incorporating non-monotone multi dimensional user preferences. The algorithm is based on an analysis of theoretical foundations for real world route planning problems and leads to a new approach of how recommendation and route generation subsystems should be coupled to increase user satisfaction.
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Paper Nr: 309
Title:

Pedagogical System in Virtual Environments for High-Risk Sites
Authors:

Kahina Amokrane and Domitile Lourdeaux
Abstract: Training at high risk sites (SEVESO sites) has many difficulties regarding potential risks, high training costs, etc. Virtual Environments for Training/Learning (VET/L) are best suited to overcome such difficulties. In this work, we have developed a collaborative VET/L where a learner with other autonomous virtual agents can work together to achieve a specific goal. We equipped this environment with an Intelligent Tutoring System, HERA, allowing to track several learners simultaneously, and to show them the consequences of their errors. HERA provides relevant feedback to learners, in real time or in a replay mode, thanks to its pedagogical model and module. This feedback depends on predefined pedagogical rules based on learners' level, their errors, the pedagogical goal, etc. In this paper, we present our system's architecture. Then, we give a detailed description of the pedagogical model, and we explain the pedagogical module functionalities.
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Paper Nr: 314
Title:

EXTRACTING CASE-BASED ANSWERS FROM CLOSED PROOF-TREES
Authors:

Isabel Gomes Barbosa, Isabel G. Barbosa and Newton J. Vieira
Abstract: A question of the form “Find X such that P(X) is true” may produce in the most usual inference systems an answer that has the general form P(T1) V P(T2) V . . . V P(Tk). If we have k ≥ 2, the answer is then termed a disjunctive answer. In some scenarios, a disjunctive answer of this form may be considered too imprecise to help the user in his activities. However, an answer which specifies the cases in which each element P(Ti) is true would be a perfectly appropriate answer to the question. In this paper, we propose an algorithm that, from a deduction of P(T1) V P(T2) V . . . V P(Tk), k ≥ 2, on the form of a proof-tree, extracts a case-based answer to the exact same question. A case-based answer is an answer given in terms of a finite number of cases, each one implying a non-disjunctive answer P(Ti), 1 ≤ i ≤ k, to the user’s question.
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Paper Nr: 317
Title:

MOVIE RECOMMENDATION WITH K-MEANS CLUSTERING AND SELF-ORGANIZING MAP METHODS
Authors:

Eugene Seo and Ho-Jin Choi
Abstract: In this paper, we apply K-means and Self-Organizing Map (SOM) methods for the recommendation system. We explain each method in movie recommendation, and compare their performance in the sense of prediction accuracy and learning time. Our experimental results with given Netflix movie datasets demonstrates how SOM performs better than K-means to give precise prediction of movie recommendation with discussion, but it needs to be solved for the overall time of computation .
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Paper Nr: 322
Title:

Intelligent vision for mobile agents: Contour maps in real time
Authors:

John Morris, Tariq Khan and Khurram Jawed
Abstract: Real time interpretation of scenes is a critical capability for fast-moving intelligent vehicles. Generation of contour maps from stereo disparity maps is one technique which allows rapid identification of objects. Here, we describe an algorithm for generating contour maps from disparity maps produced using an implementation of the Symmetric Dynamic Programming Stereo algorithm in an FPGA. The algorithm has three steps: (a) a median filter is applied to the disparity map to remove the streaks characteristic of dynamic programming algorithms, (b) irrelevant pixels in the centre of regions are marked and (c) selected contours outlined. Results for high resolution images (~ 1Mpixel) show that a number of critical contours can be generated in less than 30ms permitting object outlining at video frame rates. The algorithm is easily parallelized and we show that multiple core processors can be used to increase the number of contours that can be generated.
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Paper Nr: 329
Title:

INFORMATION UNCERTAINTY TO COMPARE QUALITATIVE REASONING SECURITY RISK ASSESSMENT RESULTS

Authors:

Gregory Chavez, Brian Key, David Zerkle and Daniel Shevitz
Abstract: The security risk associated with malevolent acts such as those of terrorism are often void of the historical data required for a traditional PRA. Most information available to conduct security risk assessments for these malevolent acts is obtained from subject matter experts as subjective judgements. Qualitative reasoning approaches such as approximate reasoning and evidential reasoning are useful for modeling the predicted risk from information provided by subject matter experts. Absent from these approaches is a consistent means to compare the security risk assessment results. This paper explores using entropy measures to quantify the information uncertainty associated with conflict and non-specificity in the predicted reasoning results. Extensions of previous entropy measures are presented here to quantify the non-specificity and conflict associated with security risk assessment results obtained from qualitative reasoning models.
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Paper Nr: 332
Title:

Where are you from? Tell me HOW you write and I will tell you WHO you are.
Authors:

Marta R. Costa-Jussà, Rafael E. Banchs and Joan Codina
Abstract: The main goal of this study is evaluating the feasibility of predicting the country, the age and the gender of a social network user, given his/her posts in one or several forums. The study was conducted with MySpace forums in Spanish language, aiming at classifying and extracting demographic information along with age and gender differences. The preliminary results presented and discussed here show some interesting conclusions about the possibility of inferring socio-demographic information from written material in the Web 2.0.
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Paper Nr: 343
Title:

A MOLECULAR CONCEPT OF MANAGING DATA (POSITION PAPER)
Authors:

Christoph Schommer
Abstract: The following (position) paper follows the concept of the field of Artificial Life and argues that the (relational) management of data can be understood as a chemical model. Whereas each data itself is consistent with atomic entities, each combination of data corresponds to a (artificial) molecular structure. For example, an attribute D inside a relational system can be represented by a nucleus alpha(D) sharing a cloud of values, which consists of so-called valectrons (the values for the column D). By using reaction rules like the selection of tuples or projection of attributes, a retrieve of molecules can be achieved quite easily. Advantages of the chemical model are no data types, a fast data access, and the associative nature of the molecules: this automatically supports a direct identification of patterns in the sense of data mining. A disadvantage is the need for restructuring that must eventually be done, because the incoming data stream is allowed to influence the chemical model. With this position paper, we present our basic concept.
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Paper Nr: 354
Title:

LSVF: LEAST SUGGESTED VALUE FIRST - A New Search Heuristic To Reduce The Amount Of Backtracking Calls In CSP
Authors:

Cleyton Rodrigues, Marcos Aurélio Almeida da Silva, Eric G. Dantas and Ryan Azevedo
Abstract: Along the years, many researches in the Artificial Intelligence (AI) field seek for new algorithms to reduce drastically the amount of memory and time consumed for general searches in the family of constraint satisfaction problems. Normally, these improvements are reached with the use of some heuristics which either prune useless tree search branches or even “indicate” the path to reach the solution (many times, the optimal solution) more easily. Many heuristics were proposed in the literature, like Static/ Dynamic Highest Degree heuristic (SHD/DHD), Most Constraint Variable (MCV), Least Constraining Value (LCV), and while some can be used at the pre-processing time, others just at running time. In this paper we propose a new pre-processing search heuristic to reduce the amount of backtracking calls, namely the Least Suggested Value First (LSVF). LSVF emerges as a practical solution whenever the LCV can not distinguish how much a value is constrained. We present a pedagogical example to introduce the heuristics, realized through the general Constraint Logic Programming CHRv, as well as the preliminary results.
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Paper Nr: 358
Title:

Achieving Robustness in Adaptive Systems - Can Hierarchies Help?
Authors:

Dragana Laketic and Gunnar Tufte
Abstract: In this paper we present the latest research directions within the investigation of adaptive autonomous systems. Originally inspired by biological solutions for performing adaptive processes, we have engaged into investigating organisation of living systems with the aim of extracting useful principles for man--made systems. The work so far has demonstrated in simulation how principles of endocrine system can be used for the initiation and support of adaptive processes until the adaptation to new environmental fluctuation is achieved. Our current research considers robustness of the system and sets the stage for the investigation into hierarchical organisation of such a system. The main question we ask is if the robustness of the proposed system could be improved if hierarchies in its architecture and (or) functional operation are taken into account.
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Paper Nr: 361
Title:

Discovering n-ary timed relations from sequences
Authors:

Nabil Benayadi
Abstract: The goal of this position paper is to show the problems with most used timed data mining techniques for discovering temporal knowledge from a set of timed messages sequences. Two of the main problems with these techniques is the sensitivity of the algorithms to the value of their parameters and the too large number of discovered patterns. We argue that these problems can be avoided using another technique called TOM4L(Timed Observations Mining for Learning). A very simple example is used to show that the TOM4L process can generate significantly better results than produced by state-of-the-art timed data mining techniques. The results obtained with an application on very complex real world system is presented to show the operational character of the TOM4L.
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Paper Nr: 364
Title:

The Role of Sequences for Incremental Learning
Authors:

Susanne Wenzel and Lothar Hotz
Abstract: In this paper, we point out the role of sequences of samples for training an incremental learning method. We define characteristics of incremental learning methods to describe the influence of sample ordering on the performance of a learned model. We show the influence of sequence for two different types of incremental learning. One is aimed on learning structural models, the other on learning models to discriminate object classes. In both cases, we show the possibility to find good sequences before starting the training.
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Paper Nr: 26
Title:

EVOLVING ROBUST ROBOT CONTROLLERS FOR CORRIDOR FOLLOWING USING GENETIC PROGRAMMING
Authors:

Bart Wyns, Bert Bonte and Luc Boullart
Abstract: Designing robots and robot controllers is a highly complex and often expensive task. However, genetic programming provides an automated design strategy to evolve complex controllers based on evolution in nature. We show that, even with limited computational resources, genetic programming is able to evolve efficient robot controllers for corridor following in a simulation environment. Therefore, a mixed and gradual form of layered learning is used, resulting in very robust and efficient controllers. Furthermore, the controller is successfully applied to real environments as well.
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Paper Nr: 31
Title:

A TREE LEARNING APPROACH TO WEB DOCUMENT SECTIONAL HIERARCHY EXTRACTION
Authors:

F. C. Pembe and Tunga Gungor
Abstract: There is an increasing availability of documents in electronic form due to the widespread use of the Internet. Hypertext Markup Language (HTML) which is mostly concerned with the presentation of documents is still the most commonly used format on the Web, despite the appearance of semantically richer markup languages such as XML. Effective processing of Web documents has several uses such as the display of content on small-screen devices and summarization. In this paper, we investigate the problem of identifying the sectional hierarchy of a given HTML document together with the headings in the document. We propose and evaluate a learning approach suitable to tree representation based on Support Vector Machines.
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Paper Nr: 45
Title:

Fingerprint Identification - a Support Vector Machine Approach
Authors:

Terje Kristensen
Abstract: In this work a hybrid technique for classification of fingerprint identification has been developed to decrease the matching time. For classification a Support Vector Machine is described and used. Automatic Fingerprint Identification Systems are widely used today, and it is therefore necessary to find a classification system that is less time-consuming. The given fingerprint database is decomposed into four different subclasses and a SVM algorithm is used to train the system to do correct classification. The classification rate has been estimated to about 87.0 % of unseen fingerprints. The average matching time is decreased with a factor of about 3.5 compared to brute force search applied.
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Paper Nr: 49
Title:

Organizing and Planning the ASIC Design Process by Means of a Multi-Agent System
Authors:

Jana Blaschke, Wolfgang Rosenstiel and Christian Sebeke
Abstract: Because of constantly improving technologies, the complexity of Integrated Circuits (ICs) is continuously increasing. Consequently IC design becomes continously more challenging and complex. A huge number of different possible design flows exists, delimited by different constraints. The design flow dynamically changes as recursions between design tasks occur. An approach that allows a fast and efficient ASIC design and that can deal with this huge complexity and dynamics is needed. Therefore we propose a methodology based on a multi-agent simulation combined with global and local scheduling techniques to construct a time-dependent, detailed model of the ASIC design process, which permits an extensive analysis and efficient organization.
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Paper Nr: 51
Title:

PARACONSISTENT NEGATION AND CLASSICAL NEGATION IN COMPUTATION TREE LOGIC
Authors:

Norihiro Kamide and Ken Kaneiwa
Abstract: A paraconsistent computation tree logic, PCTL, is obtained by adding paraconsistent negation to the standard computation tree logic CTL. PCTL can be used to appropriately formalize inconsistency-tolerant temporal reasoning. A theorem for embedding PCTL into CTL is proved. The validity, satisfiability, and modelchecking problems of PCTL are shown to be decidable. The embedding and decidability results indicate that we can reuse the existing CTL-based algorithms for validity, satisfiability and model-checking. An illustrative example of medical reasoning involving the use of PCTL is presented.
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Paper Nr: 54
Title:

Control Model of Domotic Systems Based on Ontologies
Authors:

Pablo Valiente Rocha and Adolfo Lozano-Tello
Abstract: In the present paper we introduce the model process of an expert system for the control of domotic installations based on ontologies and SWRL rules. From the domotic system database where the attribute values of each device are stored, a background process converts these values into instances of the ontology representing the system. From these instances, a software application ─known as DomoRules─ allows creating production rules in SWRL language that will be useful to regulate the system. IntelliDomo draws inferences from the ontology and the SWRL rules by using behaviour parameters previously indicated by the user, so the state of the physic devices in the domotic system can be modified in real time.
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Paper Nr: 57
Title:

COLLECTING KNOWLEDGE AND LEARNING LANGUAGES WITH TOWERS OF KNOWLEDGE GAME
Authors:

Dilyana Budakova, Mariyana Ivanova and Lydmil Dakovski
Abstract: The article treats the problem of deriving and accumulating knowledge and data about people’s everyday real-world knowledge as a background for the process of learning languages through games. The development of a game and the introduction of a virtual agent–assistant into the game are supposed to increase the interest among the users, to stimulate their motivation for language practice, and, at the same time, to increase the quantity and the quality of accumulated knowledge. The results from a survey, conducted among users, have been analyzed and generalized in this paper.
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Paper Nr: 76
Title:

RESEARCH AND DEVELOPMENT OF CONSCIOUS ROBOT
Authors:

Takashi Komatsu and Junichi Takeno
Abstract: The authors are trying to transplant a function similar to human consciousness into a robot to elucidate the mystery of human consciousness. While there is no universally accepted definition of consciousness, we believe that a consistency of cognition and behavior generates consciousness, based on the knowledge we gained in such fields as brain science and philosophy in the course of our study. Based on this idea, we have developed a module, named MoNAD, which comprises recurrent neural networks and can be used to elucidate the phenomena of human consciousness. We focused on mirror image cognition and imitation behavior, which are said to be high-level functions of human consciousness, and conducted experiments that had the robot imitate the behavior of its mirror image. This paper reports on the results of our additional tests using a new type of robot and basic experiments on discriminating a part of the self from another robot. We found that our experiments on mirror image cognition did not depend on the type of robot used, however the time delay when transmitting control signals to the other robot was an important factor that affected our evaluation of the robot’s discriminating the self from the other robots.
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Paper Nr: 86
Title:

Using Word Sense Disambiguation to Determine the Polarity of Opinions
Authors:

Tamara M. Wanton, Aurora P. Porrata, Andres Montoyo and Alexandra Balahur

Abstract: In this paper, we present an unsupervised method for determining the polarity of opinions. It uses a word sense disambiguation algorithm to determine the correct sense of the words in the opinion. The method is also based on SentiWordNet and General Inquirer to determine the polarity of the senses. Due to the characteristics of these external resources, the proposed method does not depend on the knowledge domain and can be extended to other languages. In the evaluation carried out over the SemEval Task No. 14: Affective Text data our method outperforms both unsupervised and supervised systems presented in this task.
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Paper Nr: 89
Title:

The Solution of Discrete Constraint Problems Using Boolean Models
Authors:

Bernd Steinbach and Christian Posthoff
Abstract: The use of Boolean models for discrete constraint problems has been tried at several occasions, it was, however, not recognized as efficient (Rossi et al., 2006). The solution methods were dominated by using decision trees together with depth-first or breadth-first search and/or resolution algorithms. In this paper we will show the use of ternary vectors for the solution of SAT-problems and all the problems that can be modeled by means of SAT-equations. They are an appropriate data structure representing sets of Boolean vectors. They also allow to include problem-relevant knowledge into the problem-solving process at an early point of time. The respective set operations (mainly the intersection) can be executed in a bit-parallel way (64 bits at present). For larger problems the processing can be transferred to processors working fully in parallel. There is no need for any search algorithms. The approach always finds all solutions of the problem without consideration of special cases (i.e. no solution, one solution, all solutions). Some examples are used to illustrate the approach or have been published before (Sudoku, Queen’s problems on the chessboard, node bases in graphs, graph-coloring problems).
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Paper Nr: 97
Title:

Integrating Logical and Sub-symbolic Contexts of Reasoning
Authors:

Tarek Richard Besold and Stefan Mandl
Abstract: We propose an extension of the heterogeneous multi-context reasoning framework by G. Brewka and T. Eiter that in addition to logical contexts of reasoning also incorporates sub-symbolic contexts of reasoning. The main findings of the paper are a simple extension of the concept of bridge rules to the sub-symbolic case and the concept of bridge rule models that allows for a straightforward enumeration of all equilibria of multicontext systems. We illustrate our approach with two examples from the fields of text and image classification.
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Paper Nr: 99
Title:

A STUDY OF A CONSCIOUS ROBOT
Authors:

Soichiro Akimoto and Junichi Takeno
Abstract: The authors are developing a robot that has consciousness, emotions and feelings like humans. As we make progress in this study, we look forward to deepening our understanding of human consciousness and feelings. So far, we have succeeded in representing consciousness in a robot, evolved this conscious system by adding the functions of emotions and feelings, and successfully performed mirror image cognition experiments using the robot. Emotions and feelings in a robot are, like those of humans, basic functions that can enable a robot to avoid life-threatening situations. We believe that consistency of cognition and behavior generates consciousness in a robot. If we can detect what happens in the robot when this consistency is lost, we may be able to develop a robot that is capable of discriminating between what it has learned and what it has not learned. Furthermore, anticipate that the robot may eventually be able to feel a “pain of the heart.” This paper reports on autonomous detection by a robot of non-experienced phenomena, or awareness of the unknown, using the function of consciousness embedded in the robot. If the robot is capable of detecting unknown phenomena, it may be able to continually accumulate experiences by itself.
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Paper Nr: 101
Title:

TOWARDS E-CONVIVIALITY IN WEB-BASED SYSTEMS
Authors:

Sascha Kaufmann and Christoph Schommer
Abstract: Our belief is that conviviality is a concept of great depth that plays an important role in any social interaction. A convivial relation between individuals is one that allows participating individuals to behave and interact with each other following a set of conventions either shared, commonly agreed upon or at least understood. This presupposes implicit or an explicit regulation mechanism based on consensus or social contracts and applies to the behaviours and interactions of participating individuals. With respect to web-based systems, an applicable social attribute is to assist another user, guide him/her in unclear situations and help him in making the right decision whenever a conflict arises. Such a convivial social biotope deeply depends on both implicit and explicit co-operation and collaboration of natural users inside a community. An individual conviviality may benefit from the ``wisdom of the crowd'', which means that a dynamic understanding of the user's behaviours heavily influences the individual well being of other persons. To achieve that, we present the system CUBA which targets at user profiling while making a stay convivial via recommendations.
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Paper Nr: 104
Title:

An Efficient Algorithm to Estimate Real-Time Traffic Information Based on Multiple Data Sources
Authors:

Zhu Tongyu, Lv Weifeng, Ma Dianfu, Du Bowen, Liang Yun and Du Bowen
Abstract: Gathering traffic congestion information from all available sources to provide real-time traffic information not only makes reliable traffic predictions for management center, but also supports travelers to help guiding their transit decision. However, the key issue is that the quality of existing multiple traffic data sources are uncertain, and how to use them for performing trusty travel time estimation is a question. In this paper, a novel algorithm is proposed to address this problem. Firstly, through analyzing large amounts of traffic data, the reliability of evidence and its relationship with road network are defined in spatio-temporal dimension, and then, after using an improvement aggregation method based on Dempster-Shafer evidence theory, the optimized evidences are selected to estimate each link’ s average link travel time. Comparative experiments of the real test-vehicle scheduling signals and real-time system data (supported by some 15000 floating cars and 320 loop detectors) indicate that the new algorithm is proved to be both reasonable and practical. It can be applied in real-time systems to manage large amount of data.
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Paper Nr: 113
Title:

Maximum Tolerance and Maximum Greatest Tolerance
Authors:

Xavier Molinero and Josep Freixas
Abstract: An important consideration when applying neural networks is the sensitivity to weights and threshold in strict separating systems representing a linearly separable function. Two parameters have been introduced to measure the relative errors in weights and threshold of strict separating systems: the tolerance and the greatest tolerance. Given an arbitrary separating system we study which is the equivalent separating system that provides maximum tolerance or/and maximum greatest tolerance.
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Paper Nr: 118
Title:

Enhancing Local-Search Based SAT Solvers with Learning Capability
Authors:

Ole-Christoffer Granmo and Noureddine Bouhmala
Abstract: The Satisfiability (SAT) problem is a widely studied combinatorial optimization problem with numerous applications, including time tabling, frequency assignment, and register allocation. Among the simplest and most effective algorithms for solving SAT problems are stochastic local-search based algorithms that mix greedy hill-climbing (exploitation) with random non-greedy steps (exploration). This paper demonstrates how the greedy and random components of the well-known GSAT Random Walk (GSATRW) algorithm can be enhanced with Learning Automata (LA) based stochastic learning. The LA enhancements are designed so that the actions that the LA chose initially mimic the behavior of GSATRW. However, as the LA explicitly interact with the SAT problem at hand, they learn the effect of the actions that are chosen, which allows the LA to gradually and dynamically shift from random exploration to goal-directed exploitation. Randomized and structured problems from various domains, including SAT-encoded Bounded Model Checking Problems, Logistics Problems, and Block World Planning Problems, demonstrate that our LA enhancements significantly improve the performance of GSATRW, thus laying the foundation for novel LA-based SAT solvers.
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Paper Nr: 126
Title:

Branching-time versus linear-time: A cooperative and feasible approach
Authors:

Norihiro Kamide
Abstract: A new temporal logic called linear-time computation tree logic (LCTL) is obtained from computation tree logic (CTL) by adding some modified versions of the temporal operators of linear-time temporal logic (LTL). A theorem for embedding LCTL into CTL is proved. The model-checking, validity and satisfiability problems of LCTL are shown to be deterministic PTIME-complete, EXPTIME-complete and deterministic EXPTIME-complete, respectively.
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Paper Nr: 129
Title:

A New Method using Declinable Words and Concurrent Words to create a large number of FA Words
Authors:

Elsayed Atlam
Abstract: Readers can know the subject of many document fields by reading only some specific Field Association (FA) words. Document fields can be decided efficiently if there are many rank 1 FA words (words that direct connect to terminal fields) and if the frequency rate is high. This paper proposes a new method for increasing rank 1 FA words using declinable words and concurrent words which relate to narrow association categories and eliminate FA word ambiguity. Concurrent words become Concurrent Field Association Words (CFA words) if there is a little field overlap. Usually, efficient CFA words are difficult to extract using only frequency, so this paper proposes weighting according to degree of importance of concurrent words. The new weighting method causes Precision and Recall to be higher than by using frequency alone. Moreover, combining CFA words with FA words allow easy search of fields which can not be searched by using only FA words.
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Paper Nr: 146
Title:

Multi Resolution Forecasting Method for Short Length Time Series Data Using Neural Networks
Authors:

S. Arash Sheikholeslam, Arash Sheikholeslam and Pouya Bidram
Abstract: In this paper a new multi-resolution approach for time series forecasting based on a composition of three different types of neural networks is introduced and developed. A comparison between this method and 3 ordinary neural network based forecasting methods is obtained experimentally.
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Paper Nr: 149
Title:

Reasoning about bounded time domain: An alternative to NP-complete fragments of LTL

Authors:

Norihiro Kamide
Abstract: It is known that linear-time temporal logic (LTL) is one of the most useful logics for reasoning about time and for verifying concurrent systems. It is also known that the satisfiability problem for LTL is PSPACE-complete and that finding NP-complete fragments of LTL is an important issue for constructing efficiently executable temporal logics. In this paper, an alternative NP-complete logic called bounded linear-time temporal logic is obtained from LTL by restricting the time domain of temporal operators.
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Paper Nr: 153
Title:

RULE BASED MODELLING OF IMAGES SEMANTIC CONCEPTS

Authors:

Stefan Udristoiu, Anca L. Ion and Dan Mancas
Abstract: In this paper we study the possibilities to discover correlations between visual primitive and high-level characteristics of images, meaning the extraction of semantic concepts. The design and developing of algorithms for image semantic annotation are the main contribution of this paper. The proposed methods are based on developing algorithms that automatically discover semantic rules to identify image categories. A semantic rule is a combination of semantic indicator values that identifies semantic concepts of images. Some models for representing the images and rules are also developed. The annotation methods are not limited to any specific domain and they can be applied in any field of digital imagery.
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Paper Nr: 155
Title:

A MULTI-AGENT MPC ARCHITECTURE FOR DISTRIBUTED LARGE SCALE SYSTEMS
Authors:

Valeria Javalera Rincón, Bernardo Morcego and Vicenç Puig Cayuela
Abstract: In the present work, techniques of Model Predictive Control (MPC), Multi Agent Systems (MAS) and Reinforcement Learning (RL) are combined to develop a distributed control architecture for Large Scale Systems (LSS). This architecture is multi-agent based. The system to be controlled is divided in several partitions and there is an MPC Agent in charge of each partition. MPC Agents interact over a platform that allows them to be located physically apart. One of the main new concepts of this architecture is the Negotiator Agent. Negotiator Agents interact with MPC Agents which share control variables. These shared variables represent physical connections between partitions that should be preserved in order to respect the system structure. The case of study, in which the proposed architecture is being applied and tested, is a small drinking water network. The application to a real network (the Barcelona case) is currently under development.
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Paper Nr: 166
Title:

Predicting traffic flow in road networks using Bayesian networks with data from an optimal plate scanning device location
Authors:

Santos Sánchez-Cambronero, José María Menéndez, Ana Rivas and Inmaculada Gallego
Abstract: This paper deals with the problem of predicting route flows (and hence, Origin-Destination (OD) pair and link flows) and updating these predictions when plate scanned information becomes available. To this end, a normal Bayesian network is built which is able to deal with the joint distribution of route and link flows and the flows associated with all possible combinations of scanned link flows and associated random errors. The Bayesian network provides the joint density of route flows conditioned on the observations, which allow us not only the independent or joint predictions of route flows, but also probability intervals or regions to be obtained. A procedure is also given to select the subset of links to be observed in an optimal way. An example of application illustrate the proposed methodology and shows its practical applicability and performance.
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Paper Nr: 167
Title:

THE OBSERVABILITY PROBLEM IN TRAFFIC MODELS. AN ALGEBRAIC STEP BY STEP METHOD
Authors:

Pilar Jiménez, Santos Sánchez-Cambronero, Inmaculada Gallego and Ana Rivas
Abstract: This article deals with the problem of observability of traffic networks, understanding as such the problem of identifying if a set of available flow measurements is sufficient to estimate the remaining flows in the network, OD-pair or link flows. An algebraic method for solving the observability problems is given. Specifically, a step by step procedure allowing updating the information once each item of information (OD-pair or link flow) becomes available. The method is illustrated by its application to a simple network. The results show that the proposed method provide useful information on which OD pair or link flows are informative on other OD pair and link flows, and that the method is applicable to large networks due to its numerical robustness and stability.
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Paper Nr: 170
Title:

IMPROVING AIRCRAFT MAINTENANCE WITH INNOVATIVE PROGNOSTICS AND HEALTH MANAGEMENT TECHNIQUES. Case of study: brake wear degradation.
Authors:

Susana Ferreiro and Aitor Arnaiz
Abstract: Maintenance is going through to major changes in a lot of activity fields where the current maintenance strategy must adjust to the new requirements. The aeronautics industry belongs to one these activity fields which are trying to carry out important changes around its maintenance strategy. It needs to minimize the cost for the maintenance support and to increase its operational reliability and availability (avoiding delays, cancellations, etc) which would lead to a further decrease in costs. However, to support this change, it requires transforming the traditional corrective maintenance practice of “fail and fix” to “prevent and predict”. The aim of this article is to show the usefulness and the benefits of innovative techniques such as Bayesian Networks and Reliability Analysis to support an intelligent function “decision support”, the basis for the new type of maintenance strategy based on prediction and prognosis. It helps to achieve a maximum optimization of resources and operational availability while minimizing economic costs, and replaces the current maintenance carried out in the aircraft industry up to now.
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Paper Nr: 181
Title:

Microservices: lightweight service descriptions for REST architectural style
Authors:

José Ignacio Fernández-Villamor, Fernández A. Iglesias and Mercedes Garijo
Abstract: Current web has a vast number of applications available that offer users a wide domain of services. Most services, however, cannot be machine processed, which limits service composition for application and mashup development. Research on Semantic Web Services contributes to the improvement of interoperability and composition of applications and services. Many approaches cover service description by following paradigms such as Web Services and REST architectural style, allowing describing any kind of service for its use by an automatic agent, but sometimes using these solutions can be a time-consuming task. This paper introduces Microservices, a lightweight service classification framework for REST architectural style. Microservices do not attempt to describe every possible service, but to provide a way to describe a set of services in a simple way. Microservice descriptions consist of a set of terms that represent service features. After describing features semantically, microservices framework allows generating detailed service descriptions, which allows reusing common feature descriptions across different services. A use case that adapts heterogeneous search services to produce a standard interface using microservices is described.
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Paper Nr: 193
Title:

NEURAL NETWORKS FOR THE MODELING OF CONCENTRATION OF CHEMICALS
Authors:

José Torrecilla
Abstract: Recently, biosensors based on carbon nanotubes have gained considerable attention because of their novel properties such as their high surface area, electrical conductivity, good chemical stability and extremely high mechanical strength, among others. Nevertheless, to extract the most relevant information from those huge databases formed by the output of biosensors, statistical techniques are required. In the last decade, given the characteristics of neural networks (NNs), one of the most important and widely applied techniques is based on them. Here, successful applications of NNs as chemometric tools in different types of sensors are studied. In particular, describing the uses of NNs in the quantification of ionic liquids and hydrocarbons in their quaternary mixtures, lycopene and β-carotene in food samples (by sensors), poliphenolic compounds (hazardous materials in olive oil mill wastewater, by biosensors), glucose, uric and ascorbic acids in biological mixtures (by nanobiosensors). In general, the mean prediction error values are comparable with those values in other non portable commercial analytical equipment.
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Paper Nr: 200
Title:

INTEGRATING POINTING GESTURES INTO A SPANISH–SPOKEN DIALOG SYSTEM FOR CONVERSATIONAL SERVICE ROBOTS
Authors:

Héctor Avilés, Iván Meza, Hector Aviles, Wendy Aguilar, Ivan Meza and Luis Pineda
Abstract: In this paper we present our work on the integration of human pointing gestures into a spoken dialog system in Spanish for conversational service robots. The dialog system is composed by a dialog manager, an interpreter that guides the spoken dialog and robot actions, in terms of user intentions and relevant environment stimuli associated to the current conversational situation. We demonstrate our approach by developing a tour–guide robot that is able to move around its environment, visually recognize informational posters, and explain sections of the poster selected by the user via pointing gestures. This robot also incorporates simple methods to qualify confidence in its visual outcomes, to inform about its internal state, and to start error–prevention dialogs whenever necessary. Our results show the reliability of the overall approach to model complex multimodal human–robot interactions.
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Paper Nr: 219
Title:

HOW TO LEARN A LEARNING SYSTEM. Automatic Decomposition of the Multiclass Task and Probability Estimates
Authors:

Cristina G. Cifuentes and Marc Sturzel
Abstract: Multiclass classification is the core issue of many pattern recognition tasks. In some applications, not only the predicted class is important but also the confidence associated to the decision. This paper presents a complete framework for multiclass classification that recovers probability estimates for each class. It focuses on the automatic configuration of the system so that no user-provided tuning is needed. No assumption about the nature of data or the number of classes is done either, resulting in a generic system. A suitable decomposition of the original multiclass problem into several biclass problems is automatically learnt from data. State-of-the-art biclass classifiers are optimized and their reliabilities are assessed and considered in the combination of the biclass predictions. Quantitative evaluations on different datasets show that the automatic decomposition and the reliability assessment of our system improve the classification rate compared to other schemes, as well as it provides probability estimates of each class. Besides, it simplifies considerably the user effort to use the framework in a specific problem, since it adapts automatically.
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Paper Nr: 226
Title:

Digital recording of TV broadcasting and advertisement detection
Authors:

Alberto Gómez, Javier Ortiz and Alberto Gómez
Abstract: In this paper we provide an integrated approach for detecting commercial advertisements in TV broadcasting for a digital recording of old TV shows. This approach uses Artificial Vision for detecting the TV logo in the video streams. The results of comprehensive experiments on a heterogeneous data emission set of 300 minutes of video taken from 5 different sources are reported. Our method provides almost 98% correct detection and elimination of the advertisements for a digital recording.
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Paper Nr: 236
Title:

Capturing user's preferences using a genetic algorithm. Determining Essential and Dispensable Item Attributes
Authors:

Valero Soledad, Argente Estefanía and Vicente Botti
Abstract: Determining the most desired product attributes would be crucial for companies that want to offer their clients those products which best fit their preferences. In this work, a genetic approach is employed for establishing the appropriate attribute weights of movies, determining which movie attributes are essential or dispensable for users in their selection process. The obtained weights are employed to predict user's ratings for a test set of movies, proving that the obtained parameters really describe their preferences.
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Paper Nr: 240
Title:

An Agent-based Optimization Approach for Distributed Project Scheduling in Supply Chain with Partial Information Sharing
Authors:

Guorui Jiang, Hanlin Zhang and Tiyun Huang
Abstract: This paper focuses on the optimization problem of distributed project scheduling in the supply chain network which is made up of order manager, service brokers and service suppliers. Based on the initial scheduling by bids of service brokers, we present a heuristic approach with agent negotiation mechanism for the problem. The approach seeks optimal schedule by distributed negotiations, which apply the agent negotiation mechanism and share limited information, between order manager and brokers. Computational experiments show the approach is effective with good optimization performance.
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Paper Nr: 241
Title:

A reinforcement learning approach for multiagent navigation
Authors:

Fernando Fernandez, Francisco Martinez-Gil, Fernando Fernandez, Miguel Lozano, Fernando Barber and Francisco Grimaldo
Abstract: This paper presents a Q-Learning-based multiagent system oriented to provide navigation skills to simulation agents in virtual environments. We focus on learning local navigation behaviours from the interactions with other agents and the environment. Interactions are essential, as they will help the agents to learn the variability of different navigational situations, that will be possibly faced during simulation. We adopt an environment-independent state space representation to provide the required scalability of such kind of systems. In this way, we evaluate whether the learned action-value functions can be transferred to other agents to increase the size of the group without loosing behavioural quality. We explain the learning process defined and the the results of the collective behaviours obtained in a well-known experiment in multiagent navigation: the exit of a place through a door.
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Paper Nr: 250
Title:

Using Lightweight Knowledge Modelling To Improve Proactive Information Delivery
Authors:

Oleg Rostanin, Heiko Maus, Takeshi Suzuki and Kaoru Maeda
Abstract: The current work presents an integrated solution for task-centric proactive information delivery (PID) in agile knowledge working (AKW) environments. The approach exploits a lightweight incremental modeling of task relevant knowledge domains and process know-how using concept maps together with concept-based task tagging to improve the quality of PID results. The feasibility of the described approach was proved during the joint research project TaskNavigator conducted by Ricoh Co. Ltd and DFKI GmbH.
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Paper Nr: 263
Title:

PREDICTING BURSTING STRENGTH OF PLAIN KNITTED FABRICS USING ANN
Authors:

Pelin G. Ünal, Mustafa Üreyen and Diren M. Armakan
Abstract: In this study, the effects of yarn parameters, on the bursting strength of the plain knitted fabrics were examined with the help of artificial neural networks. In order to obtain yarns having different properties such as tenacity, elongation, unevenness, the yarns were produced from six different types of cotton. In addition to cotton type, yarns were produced in four different counts having three different twist coefficients. Artificial neural network (ANN) was used to analyze the bursting strength of the plain knitted fabrics. As independent variables, yarn properties such as tenacity, elongation, unevenness, count, twists per inch together with the fabric property number of wales and courses per cm were chosen. For the determination of the best network architecture, three levels of number of neurons, number of epochs, learning rate and momentum coefficient were tried according to the orthogonal experimental design. After the best neural network for predicting the bursting strength of the plain knitted fabrics was obtained, statistical analysis of the obtained neural network was performed. Satisfactory results for the prediction of the bursting strength of the plain knitted fabrics were gained as a result of the study.
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Paper Nr: 269
Title:

RELATIONAL SEQUENCE BASED CLASSIFICATION IN MULTI-AGENT SYSTEMS
Authors:

Grazia Bombini, Nicola Di Mauro, Stefano Ferilli and Floriana Esposito
Abstract: In multiagent adversarial environments, the adversary consists of a team of opponents that may interfere with the achievement of goals. In this domain agents must be able to quickly adapt to the environment and infer knowledge from other agents’ deportment to identify the future behaviors of opponents. We present a relational model to characterize adversary teams based on its behavior. A team’s deportment is represent by a set of relational sequences of basic actions extracted from their observed behaviors. Based on this, we present a similarity measure to classify the teams’ behavior. The sequence extraction and classification are implemented in the domain of simulated robotic soccer, and experimental results are presented.
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Paper Nr: 297
Title:

ON AMBIGUITY DETECTION AND POSTPROCESSING SCHEMES USING CLUSTER ENSEMBLES
Authors:

Mehmet Muti Soenmez, Amparo Albalate, Aparna Suchindranath, Mehmet Soenmez and David Suendermann
Abstract: In this paper, we explore the cluster ensemble problem and propose a novel scheme to identify uncertain/ambiguous regions in the data based on the different clusterings in the ensemble. In addition, we analyse two approaches to deal with the detected uncertainty. The first, simplest method, is to ignore ambiguous patterns prior to the ensemble consensus function, thus preserving the non-ambiguous data as good prototypes for any further modelling. The second alternative is to use the ensemble solution obtained by the first method to train an inherently supervised model, which is later applied to reallocate, or recluster the ambiguous patterns. In particular, we selected a model based on support vector machines (SVMs), due to their robustness with respect to other classification algorithms. A comparative analysis of the different ensemble solutions and the base weak clusterings has been conducted on five data sets: two artificial mixtures of five and seven Gaussian, and three real data sets from the UCI machine learning repository. Experimental results have shown in general a better ensemble performance when our proposed schemes to deal with ambiguities are introduced.
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Paper Nr: 316
Title:

Emotional Facial Expression Recognition From Two Different Feature Domains
Authors:

Jonghwa Kim and Frank Jung
Abstract: There has been a significant amount of work on automatic facial expression recognition towards realizing affective interfaces in human-computer interaction (HCI). However, most previous works are based on specific users and dataset-specific methods and therefore the results should be strongly dependent on their lab settings. This makes it difficult to attain a generalized recognition system for different applications. In this paper, we present efficiency analysis results of two feature domains, Gabor wavelet-based feature space and geometric position-based feature space, by applying them to two facial expression datasets that are generated in quite different environmental settings.
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Paper Nr: 345
Title:

Two-Population Genetic Algorithm: an Approach to Improve the Population Diversity
Authors:

Marcos Gestal, Juan Ramón Rabuñal, Enrique Fernández-Blanco, Julian Dorado and Daniel Rivero
Abstract: Genetic Algorithms (GAs) are a technique that has given good results to those problems that require a search through a complex space of possible solutions. A key point of GAs is the necessity of maintaining the diversity in the population. Without this diversity, the population converges and the search prematurely stops, not being able to reach the optimal solution. This is a very common situation in GAs. This paper proposes a modification in traditional GAs to overcome this problem, avoiding the loose of diversity in the population. This modification allows an exhaustive search that will provide more than one valid solution in the same execution of the algorithm
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Paper Nr: 346
Title:

SUMMARIZING AND VISUALIZING WEB PEOPLE SEARCH RESULTS
Authors:

Harumi Murakami, Hiroshi Ueda, Shin'ichi Kataoka, Yuya Takamori and Shoji Tatsumi
Abstract: People search is one major search activity on the Web. If the list of people search results is merely “person 1, person 2, . . . and so on,” users have difficulty determining which person clusters they should select. In this paper, we present a project that summarizes and visualizes Web people search results to help users select person clusters more easily. We explore three ways of summarizing people: (a) selecting terms from the extracted information, (b) combining the extracted information, and (c) obtaining information from external databases referring to the extracted information. To visualize people, we present three types of interfaces: (a) tables, (b) two-dimensional space, and (c) map interfaces. We report the two results of the project. (1) We investigated algorithms for distinguishing individuals with identical names and three ways of summarizing people: extracting keywords, prefectures and vocations; combining vocation-related information; and obtaining locations. (2) We developed prototypes to display separated individuals by three types of interfaces.
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Paper Nr: 350
Title:

QUERY-BY-EMOTION SKETCH FOR LOCAL EMOTION-BASED IMAGE RETRIEVAL
Authors:

Kyoung-Mi Lee and Young-Chang Lee
Abstract: This paper has proposed an image retrieval system by using an emotion sketch in order to retrieve images that locally hold different emotions. The proposed image retrieval system divides an image into the 17×17 non-overlapping sub-areas. In order to extract the emotion features in sub-areas, the proposed system has used the emotion colors that correspond to the 160 emotion words that suggested by the color imaging chart of H. Nagumo. By calculating the distribution of emotion colors corresponding to the emotion words from the sub-areas, the system takes the emotion word that holds the largest value among the histogram values of the emotion words of each sub-area. The image retrieval system with using the proposed emotion sketch query has demonstrated excellent retrieval precision and recall functions that are better than the global approach by evaluating the validity of the Corel database.
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Paper Nr: 356
Title:

New research lines for Max-SAT
Authors:

Federico Heras
Abstract: This paper presents the current state-of-the-art techniques for Max-SAT solving and points out new research lines in order to exploit the benefits of the novel resolution rule for Max-SAT.
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Paper Nr: 359
Title:

Arabic word sense disambiguation
Authors:

Laroussi Merhben, Anis Zouaghi and Mounir Zrigui
Abstract: In this paper we propose an hybrid system of Arabic words disambiguation. To achieve this goal we use the methods employed in the domain of information retrieval: Latent semantic analysis, Harman, Croft, Okapi, combined to the lesk algorithm. These methods are used to estimate the most relevant sense of the ambiguous word. This estimation is based on the calculation of the proximity between the current context (Context of the ambiguous word), and the different contexts of use of each meaning of the word. The Lesk algorithm is used to assign the correct sense of those proposed by the LSA, Harman, Croft and Okapi. The results found by the proposed system are satisfactory, we obtained a rate of disambiguation equal to 76%.
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Paper Nr: 360
Title:

Sound Summarizations for ALCHI Ontologies
Authors:

Sebastian Wandelt and Ralf Möller
Abstract: In the last years, the vision of the Semantic Web fostered the interest in reasoning over ever larger sets of assertional statements in ontologies. In this senario, state-of-the-art description logic reasoning systems cannot deal with real-world ontologies any longer, since they rely on in-memory structures. In these scenarios it will become more important to rely on unsound or incomplete reasoning structures, to obtain a set of candidates/obvious solutions to queries, i.e. only apply state-of-the-art reasoning systems to the computationally hard solutions. In this paper we propose a summarization-based approach which is always sound, but possibly incomplete. We think that this technique will support description logic systems to deal with the steadily growing amounts of assertional data.
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Paper Nr: 369
Title:

THE INTELLIGENT WEB
Authors:

Jose Alberto Jaen, Francisco J. Javier Del Alamo and Raquel Martinez
Abstract: Many people are working on Semantic Web with the main objective of enhancing searches on that. Our proposal is a new research strategy based on the proposal of a discrete set of semantic relations for the creation and exploitation of semantic networks on the web. To do so, we have defined in a previous paper [15] the Rhetoric-Semantic Relation (RSR) based on the results of the Rhetoric Structure Theory. We formulate a general set of RSR capable of building discourse and making it possible to express any concept, procedure or principle in terms of knowledge nodes and RSRs. These knowledge nodes can then be elaborated in the same way. This network structure in terms of RSR makes the objective of developing automatic answering systems possible as well as any other type of utilities oriented towards the exploitation of semantic structure, such as the automatic production of web pages or automatic e-learning generation.
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Paper Nr: 371
Title:

Integrating CBR and EBL in an apprentice agent
Authors:

Lei Wang, Lei Wang, Tetsuo Sawaragi, Yajie Tian and Yukio Horiguchi
Abstract: The problem in applications of case based reasoning (CBR) is its utility problem, that is, the cost of retrieving the most appropriate case from the case library for a new given problem and the cost of adapting the retrieved case for solving the new given problem. This paper proposes an approach to solve the utility problem of CBR by integrating CBR and explanation based learning (EBL) from a perspective that emphasizes the function of learning in CBR. In this paper, CBR and EBL are integrated in an apprentice agent, and the application of this apprentice agent in the robotic assembly domain is given as an example.
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Area 2 - Agents

Full Papers
Paper Nr: 115
Title:

MIPITS - An Agent Based Intelligent Tutoring System
Authors:

Egons Lavendelis and Janis Grundspenkis
Abstract: During the last decades many Intelligent Tutoring Systems (ITS) are developed to add adaptivity and intelligence to the e-learning systems. Intelligent agents and multi-agent systems are widely used to implement intelligent mechanisms for ITSs due to their characteristics. The paper presents an agent based ITS for the course “Fundamentals of Artificial Intelligence” named MIPITS. The MIPITS system is based on the holonic multi-agent architecture for ITS development. The system offers learning materials, provides practical problems and gives feedback to the learner about his/her solution evaluating his/her knowledge. The goal of the system is to realize individualized practical problem solving, which is not possible in the classroom due to the large number of students in the course. Thus, the main focus of the system is on problem solving. The system offers three types of problems: tests, state space search problems and two-player games algorithm problems. The MIPITS system is open: new types of problems can be added just by including appropriate agents in the system. The problems are adapted to the learner’s knowledge level and preferences about difficulty, size and practicality of problems.
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Paper Nr: 151
Title:

A 3D Pedestrian Simulator using a Spatial DBMS
Authors:

Hyeyoung Kim and Chulmin Jun
Abstract: Most crowd simulation models for pedestrian dynamics are based on analytical approach using experimental settings without being related to real world data. In order for the models to be adapted to real world applications such as fire evacuation or warning systems, some technical aspects first must be resolved. First, the base data should represent the 3D indoor model which contains semantic information of each space. Second, in order to communicate with the indoor localization sensors to capture the real time pedestrians and to store the simulation results for later uses, the data should be in a DBMS instead of files. The purpose of this paper is two folds. One is to suggest a DBMS-based 3D modeling approach for pedestrian simulations. The other is to improve the existing floor field based pedestrian model by modifying the dynamic field. We illustrated the data construction processes and simulations using the proposed DBMS approach and the enhanced pedestrian model.
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Paper Nr: 180
Title:

A MULTI-AGENT SYSTEM FOR INTELLIGENT BUILDING CONTROL : NORM APPROACH
Authors:

Jarunee Duangsuwan and Kecheng Liu
Abstract: Most of previous research in the intelligent buildings has proposed the controlling system that can change building environmental conditions automatically in order to save energy consumption and also to increase occupant’s satisfaction. Decreasing energy consumption and increasing occupant comport are important factors to indicate intelligent building’s performance because it is a particular way to improved productivity resulting in business benefits. By applying agent technology, the intelligent building control system provides a practical application that can minimize energy consumption levels, while keeping a satisfying response to the building occupants’ comfort. This paper proposes an abstract extended-EDA (Epistemic-Deontic-Axiologic) model which is enhanced agent’s capability in order to make decision under norms: obligations, permissions and prohibitions. The model is represented in terms of an individual agent that is prepared for the multi-agent system of intelligent building control. The model of multi-agent control system of intelligent building is proposed to combine the comfort condition control with an energy saving strategy.
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Paper Nr: 220
Title:

SELF-ADAPTIVE MULTI-AGENT SYSTEM FOR SELF-REGULATING REAL-TIME PROCESS : Preliminary Study in Bioprocess Control
Authors:

Sylvain Videau, Carole Bernon and Pierre Glize
Abstract: Bioprocesses are especially difficult to model due to their complexity and the lack of knowledge available to fully describe a microorganism and its behavior. Furthermore, controlling such complex systems means to deal with their non-linearity and their time-varying aspects. In order to overcome these difficulties, we propose a generic approach for the control of a bioprocess. This approach relies on the use of an Adaptive Multi-Agent System (AMAS), acting as the controller of the bioprocess. This gives it genericity and adaptability, allowing its application to a wide range of problems and a fast answer to dynamic modifications of the real system. The global control problem will be turned into a sum of local problems. Interactions between local agents, which solve their own inverse problem and act in a cooperative way, will enable the emergence of an adequate global function for solving the global problem while fulfilling the user’s request. An instantiation of this approach is then applied to an equation solving problem, and the related results are presented and discussed.
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Paper Nr: 229
Title:

A Pattern Approach to Modeling the Provider Selection Problem
Authors:

Jose J. Duran and Carlos A. Iglesias
Abstract: This article introduces the notion of agreement patterns, which provide a framework for modeling reusable problem solution descriptions for agreement fulfillment. In particular, the Provider Selection pattern has been identified for modeling the common problem of selecting a provider by a service consumer. The article presents the pattern structure as well as the reusable domain model and cognitive structures. Agreement patterns aim at providing reusable patterns useful for developers in multidisciplinary areas, such as Agent Technology and Service Oriented Computing.
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Paper Nr: 246
Title:

Scheduling solution for Grid Meta-brokering using the Pliant system
Authors:

Attila Kertesz and József D. Dombi
Abstract: In this paper we present some advanced scheduling techniques with a weighted fitness function for an adaptive Meta-Brokering Grid Service using the Pliant system, which enables a higher level utilization of the existing grid brokers. We construct and demonstrate the efficiency of our new algorithms in a grid simulation environment. The results given here demonstrate that the proposed novel scheduling technique produces better performance scores.
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Paper Nr: 248
Title:

AGENT-BASED SYSTEMS DESIGN FOR VIRTUAL ORGANISATIONS FORMATION
Authors:

Tiemei I. Zhang
Abstract: These days, organisations must adapt to business and technical changes which are vital in a competitive and ever-changing business environment. To meet the dynamically changing requirements, virtual organisation is widely recognised as an effective solution. Multiple agent technology has been actively discussed and recognised as the merit of flexibility and adaptability. Thus, this technology can be applied to virtual organisations. To prevent from being impeded by using incongruous approaches to designing agent-based systems, systematic approach is essential to incorporate a variety of organisation business units that are required to meet current and future needs. This paper aims to present a systematic approach including process of analysis and design to designing virtual organisations. It also demonstrates a case study for application of this approach.
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Paper Nr: 253
Title:

Model-free Learning from Demonstration
Authors:

Erik Billing, Thomas Hellström and Lars-Erik Janlert
Abstract: A novel robot learning algorithm called Predictive Sequence Learning (PSL) is presented and evaluated. PSL is a model-free prediction algorithm inspired by the dynamic temporal difference algorithm S-Learning. While S-Learning has previously been applied as a reinforcement learning algorithm for robots, PSL is here applied to a Learning from Demonstration problem. The proposed algorithm is evaluated on four tasks using a Khepera II robot. PSL builds a model from demonstrated data which is used to repeat the demonstrated behavior. After training, PSL can control the robot by continually predicting the next action, based on the sequence of passed sensor and motor events. PSL was able to successfully learn and repeat the first three (elementary) tasks, but it was unable to successfully repeat the fourth (composed) behavior. The results indicate that PSL is suitable for learning problems up to a certain complexity, while higher level coordination is required for learning more complex behaviors.
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Paper Nr: 257
Title:

INTELLIGENT AGENTS FOR SEMANTIC SIMULATED REALITIES. The ISReal Platform.
Authors:

Matthias Klusch, Philipp Slusallek, Stefan Nesbigall, Stefan Warwas, Patrick Kapahnke, René Schubotz, Matthias Klusch, Klaus Fischer and Philipp Slusallek
Abstract: Realistic virtual worlds are increasingly used for training, decision making, entertainment, and many other purposes, which require the convincing modeling and animation of virtual characters as well as the faithful behavior of devices in their environment. Intelligent Simulated Realities (ISReal) is a platform for virtual environments that are enriched with a high-level semantic description and populated by intelligent agents using Semantic Web technologies to perceive, understand, and interact with their environment. In this paper, we present the basic architecture of the ISReal platform and show the user interaction in an agent assisted learning scenario.
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Paper Nr: 261
Title:

AUCTION SCOPE, SCALE AND PRICING FORMAT: Agent-based simulation of the performance of a water quality tender
Authors:

Romy Greiner, Atakelty Hailu, Jill Windle, Romy Greiner and John Rolfe
Abstract: Conservation auctions are tender-based mechanisms for allocating contracts among landholders who are intertested in undertaking conservation activities in return for monetary rewards. These auctions have grown in popularity over the last decade. However, the services offered under these auctions can be complex and auction design and implementation features need to be carefully considered if these auctions are to perform well. Computational experiments are key to bed-testing auction design as the bulk of auction theory (as the rest of economic theory) is focused on simple auctions for tractability reasons. This paper presents results from an agent-based modelling study investigating the impact on performance of four auction features: scope of conservation activities in tendered projects; auction budget levels relative to bidder population size (scale effects); endogeneity of bidder participation; and auction pricing rules (uniform versus discriminatory pricing). The results highlight the importance of a careful consideration of scale and scope issues and that policymakers need to consider alternatives to currently used pay-as-bid or discriminatory pricing fromats. Averaging over scope variations, the uniform auction can deliver at least 25% more benefits than the discriminatory auction.
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Paper Nr: 271
Title:

COORDINATION AND ORGANISATIONAL MECHANISMS APPLIED TO THE DEVELOPMENT OF A DYNAMIC, CONTEXT-AWARE INFORMATION SERVICE
Authors:

Manel Palau, Ignasi Gómez-Sebastià, Javier Vázquez-Salceda, Juan Carlos Nieves and Luigi Ceccaroni
Abstract: A multi-agent system design-methodology is used to address the highly dynamic, regulated, complex, distributed environment of interconnected services. A framework of three interconnected levels is applied to tackle this issue relying on coordination and organisational techniques, as well as on Web-services and new methodologies to design, deploy and maintain a distributed system. This paper presents results based on a real use case: interactive community displays with touristic information and services, dynamically personalised according to user context and preferences.
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Paper Nr: 273
Title:

AGENT-BASED INTERDISCIPLINARY FRAMEWORK FOR DECISION MAKING IN COMPLEX SYSTEMS
Authors:

Francisco J.Gómez, Marina V. Sokolova and Antonio Fernández-Caballero
Abstract: We offer a framework for the creation of decision support and expert systems for complex natural domains. This is due, on the one hand, to the numerous advantages of intelligent methods of data manipulation and, on the other hand, to the abilities of the computational agents to make decentralized decisions, which are crucial for complex systems modeling and simulation.In our approach, the qualitative improvement in decision making is obtained by using computational agents and interdisciplinary approach. The frameworks combines, on the one hand, the numerous advantages of intelligent methods for data manipulation and, on the other hand, the abilities of the computational agents to make decentralized decisions, which is crucial for complex system modeling and simulation. The approach contributes to decentralization and local decision making within the standard workflow. We demonstrate our framework in a case study and discuss obtained results.
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Paper Nr: 320
Title:

A Cognitive Model for Human Behavior Simulation in EBDI Virtual Humans
Authors:

Héctor O. Aguirre, Félix F. Ramos-Corchado, Victor Fernández-Cervantes, J. O. Gutiérrez-García, Marco A. Ramos-Corchado and Daniel Thalmann
Abstract: In this paper, we present a new cognitive model based on Psychology for simulating human behavior in realistic virtual humans. To do this, we use the Minnesota Multiphasic Personality Inventory (MMPI), taking into account the personality scales defined in it to endow the virtual humans with a real personality and form a set of fuzzy rules used to obtain the emotional influences that modify virtual humans' affective state according to their personality and the events they perceive from their environment. We also implemented an EBDI-based action selection by using an event calculus definition. This action selection mechanism allows virtual humans to perform actions based on their current emotional state, their beliefs, their desires and their intentions. These actions define virtual humans' behavior for each situation they experience in the environment. As case study, we present an scenario where a male virtual human with a psychopathic personality and a female virtual human with a hysteric personality are interacting in a real way.
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Short Papers
Paper Nr: 30
Title:

COACH BOT – MODULAR E-COURSE WITH VIRTUAL COACH TOOL SUPPORT
Authors:

Ilaria Mascitti, Federica Funghi, Mikail Feituri, Luca A. Galassi and Mikail Feituri
Abstract: The COACH BOT project aims at designing and testing an innovative e-learning methodology for adult education that combines Conversational Agent Technology (chatbot) with an ad hoc designed modular learning path. The pilot e-course addresses a target group of home health care professionals (e.g. home caretakers, nurses, etc). The project’s innovation consists of the development of a collaborative e-learning environment featuring a “chatbot” or “Virtual Coach” who interacts with users through a human-like interface. The “Virtual Coach” acts as a teacher, a coach, a tutor, who supports learners “individually” during the modular e-course by providing in-depth information, assessment, case studies, technical and methodological support. The e-course curriculum is based on a personalised approach allowing learners, with help from the COACH BOT, to customize their own training path and benefit from a suitable training path that are relevant to their profession and based on their own specific needs, knowledge and skill requirements.
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Paper Nr: 63
Title:

Towards a Comprehensive Teamwork Model for Highly Dynamic Domains
Authors:

Hendrik Skubch, Roland Reichle, Michael Wagner, Kurt Geihs and Stefan Triller
Abstract: Cooperative behaviour of agents within highly dynamic and nondeterministic domains is an active field of research. In particular establishing highly responsive teamwork, where agents are able to react to dynamic changes in the environment while facing unreliable communication and sensory noise, is an open problem. Unexpectedly changing situations force agents to react and adapt under tight time-constraints. Hence they often cannot communicate or agree upon their decisions before acting upon them. We present a novel model for cooperative behaviour geared towards such domains. In our approach, the agents estimate each other's decision and correct these estimations once they receive contradictory information. We aim at a comprehensive approach for agent teamwork featuring intuitive modelling capabilities for multi-agent activities, abstractions over activities and agents, and clear operational semantics for the new model. We show experimentally that the resulting behaviour stabilises towards teamwork and can achieve a cooperative goal.
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Paper Nr: 77
Title:

Distributed Planning Through Graph Merging
Authors:

Damien Pellier
Abstract: In this paper, we introduce a generic and fresh model for distributed planning called "Distributed Planning Through Graph Merging'" (DPGM). This model unifies the different steps of the distributed planning process into a single step. Our approach is based on a planning graph structure for the agent reasoning and a CSP mechanism for the individual plan extraction and the coordination. We assume that no agent can reach the global goal alone. Therefore the agents must cooperate, i.e., take in into account potential positive interactions between their activities to reach their common shared goal. The originality of our model consists in considering as soon as possible, i.e., in the individual planning process, the positive and the negative interactions between agents activities in order to reduce the search cost of a global coordinated solution plan.
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Paper Nr: 80
Title:

Manipulating Recommendation Lists by Global Considerations
Authors:

Roie Zivan, Alon Grubshtein, Nurit Gal-Oz, Tal Grinshpoun, Amnon Meisels and Roie Zivan
Abstract: The designers of trust and reputation systems attempt to provide a rich setting for interacting users. While most research is focused on the validity of recommendations in such settings, we study means of introducing system requirements and secondary goals which we term Global Considerations. Recommendation systems are assumed to be based on a framework which includes two types of entities: service providers and users seeking services (e.g. eBay). The paper formulates a basis for manipulation of information in a manner which does not harm either. These manipulations must be carefully devised: an administrator attempting to manipulate ratings, even for the benefit of most participants, may dampen the gain of service providers, users or both. On the other hand, such changes may produce a more efficient and user friendly system, allow for the improved initialization of new service providers or upgrade existing features. The present paper formulates threshold values which define the limits of our manipulation, propose different concepts for manipulation and evaluates by simulation the performance of systems which employ our manipulations.
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Paper Nr: 92
Title:

Risk Analysis and Deployment Secuity Issues in a Multi-agent System
Authors:

Ambra Molesini, Marco Prandini, Elena Nardini and Enrico Denti
Abstract: Multi-agent systems (MASs) are a powerful paradigm which enables very effective software engineering techniques, but at the same time easily lets the designer be oblivious of the emergent security problems. This can be a critical issue, especially when MASs are exploited as an infrastructure to provide secure services. This paper performs a security analysis of such a scenario, by identifying threats and assessing risks that could interfere with the achievement of the overall application’s goal, that is access control, as a consequence of its MAS-based implementation. The process is exemplified with reference to a specific application domain, yet serves as a starting point for a broader goal: defining a general methodology for modelling the security problems surrounding the operation and application of MASs.
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Paper Nr: 93
Title:

AGENT ONTOLOGY INTEROPERABILITY APPROACH FOR MAS NEGOTIATIONS IN VIRTUAL ENTERPRISES
Authors:

G. Wang, Xiaohuan Wang, T. N. Wong and Gong Wang
Abstract: In supply chain management, a Virtual Enterprise (VE) is a dynamic alliance of partner companies. Multi-agent systems (MAS) have been introduced to facilitate negotiations among VE members. From the perspective of knowledge management, heterogeneous VE members utilize different knowledge structures and terminologies in their representative agents. To encourage their collaborative coordination and realize mutual understanding, agent ontology interoperability should be reached. In this paper, an approach for semantic ontology matching is proposed to generate correspondences among heterogeneous ontologies embedded in MAS; additionally, an ontology correspondence generation and negotiation protocol is developed to realize agent ontology interoperability in MAS negotiations.
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Paper Nr: 110
Title:

USING MOBILE AGENTS IN EEG SIGNAL PROCESSING
Authors:

Roman Moucek and Petr Šolc
Abstract: Our EEG/ERP repository contains large EEG/ERP data. The partner institutions would like to work with these data e.g. to verify their processing methods, but they cannot or they are not allowed to transfer large data collections over network. The possible solution is to use a mobile agent system. The paper briefly introduces the agent system Aglets and basic EEG processing methods implemented as mobile agents. The usability of this approach is tested on selected data files. The implementation of e-mail announcements within the mobile agent system is mentioned.
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Paper Nr: 122
Title:

A MAS-BASED NEGOTIATION MECHANISM TO DEAL WITH SATURATED CONDITIONS IN DISTRIBUTED ENVIRONMENTS
Authors:

Mauricio Paletta and Pilar Herrero
Abstract: In Collaborative Distributed Environments (CDEs) based on Multi-Agent System (MAS), agents collaborate with each other aiming to achieve a common goal. However, depending on several aspects, like for example the number of nodes in the CDE, the environment condition could be saturated / overloaded making it difficult for agents who are requesting the cooperation of others to carry out its tasks. To deal with this problem, the MAS-based solution should have an appropriate negotiation mechanism between agents. Appropriate means to be efficient in terms of the time involved in the entire process and, of course, that the negotiation is successful. This paper focuses on this problem by presenting a negotiation mechanism (algorithm and protocol) designed to be used in CDEs by means of multi-agent architecture and the awareness concept. This research makes use of a heuristic strategy in order to improve the effectiveness of agents’ communication resources and therefore improve collaboration in these environments
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Paper Nr: 136
Title:

Probabilistic Award Strategy for Contract Net Protocol in Massively Multi-Agent Systems
Authors:

Toshiharu Sugawara, Toshio Hiortsu and Kensuke Fukuda
Abstract: We propose a probabilistic award selection strategy for a contract net protocol (CNP) in massively multi-agent systems (MMASs) for effective task allocations. Recent Internet and sensor network applications require sophisticated multi-agent system technologies to enable the large amounts of software and computing resources to be effectively used. Improving the overall performance of MMASs in which thousands of agents work concurrently requires a new negotiation strategy for appropriately allocating tasks to agents. Our proposed method probabilistically selects the awardee in CNP based on the statistical difference between bid values for subtasks that have different costs. We explain how our proposed method can significantly improve the overall performance of MMASs.
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Paper Nr: 164
Title:

Cooperative Learning of BDI Elevator Agents
Authors:

Hiroaki Ueda, Yuya Takata, Yuki MIKURA and Kenichi Takahashi
Abstract: We propose a framework of cooperative learning of BDI agents. Our framework uses some kinds of agents, including a task management agent (TMA) and rational agents. TMA is designed as a learning agent. It manages assignment of tasks to rational agents. When a task is created, TMA evaluates the most useful strategy on the basis of reinforcement learning. Rational agents also evaluate the value that the task is assigned to them according to the strategy, and they give the value as their intention to TMA. Then, TMA optimally assigns the task to a rational agent by using both the value and the rough strategies, and the rational agent processes the task. In this article, we apply the proposed method to an elevator group control problem. Experiment results show that the proposed method finds better task assignment than the methods without cooperative learning.
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Paper Nr: 184
Title:

EVALUATION OF TRUST POLICIES BY SIMULATION
Authors:

Cosmin Mogo, Cosmin M. Mogos and Ina Schieferdecker
Abstract: The evolution of the World Wide Web has created a new environment where people can interact, e.g. talk to their friends, shop online, conduct business meetings, etc. Trust and trustworthiness are central notions in human interaction; in particular, they represent important criteria for every Internet user because of the multitude of choices they are faced with when choosing whom to interact with. This paper presents a simulation model implemented in Ptolemy II for the simulative analysis of trust policies in networked environments, called communication space (CS). The model reflects both the CS structure e.g. principals, roles, and event structure, and the interactions between the elements of CSs. Principal behavior is based on Markov chains and on criteria for selecting peers that initiate transactions. We investigate the efficiency of trust policies based on local observations and evaluation of interactions by examining a case study based on the popular auction site eBay.
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Paper Nr: 189
Title:

Coordination of Planning and Scheduling Techniques for a Distributed, Multi-Level, Multi-Agent System
Authors:

John Kinnebrew, Douglas Schmidt, Daniel Mack and Gautam Biswas
Abstract: Planning and scheduling for agents operating in heterogeneous, multi-agent environments is governed by the nature of the environment and the interactions between agents. Significant efficiency and capability gains can be attained by employing multiple planning and scheduling mechanisms that are each tailored to the particular agent roles. This paper presents such a framework for a global sensor web, operating as a two-level hierarchy: (1) the mission level for global coordination of complex tasks, and (2) the resource level for operation of subtasks on individual sensor networks. We describe important challenges in coordinating among agents employing two different planning and scheduling methods and develop a coordination solution for this framework. Experimental results validate the benefits of employing guided, context-sensitive coordination of planning and scheduling in such systems.
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Paper Nr: 247
Title:

RELATED WORD EXTRACTION FROM WIKIPEDIA FOR WEB RETRIEVAL ASSISTANCE
Authors:

Kentaro Hori, Ryuzo Hasegawa, Tetsuya Oishi, Tsunenori Mine, Hiroshi Fujita and Miyuki Koshimura
Abstract: This paper proposes a web retrieval system with extended queries generated from the contents of Wikipedia.By using the extended queries, we aim to assist user in retrieving Web pages and acquiring knowledge. To extract extended query items, we make much of hyperlinks in Wikipedia in addition to the related word extraction algorithm. We evaluated the system through experimental use of it by several examinees and the questionnaires to them. Experimental results show that our system works well for user's retrieval and knowledge acquisition.
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Paper Nr: 254
Title:

AN AGENT-BASED MODEL FOR RECREATIONAL FISHING MANAGEMENT EVALUATION IN A CORAL REEF ENVIRONMENT
Authors:

Atakelty Hailu, Lei Gao, Atakelty Hailu, Lei Gao and Jeff Durkin
Abstract: This paper presents an integrated agent-based model of recreational fishing behaviour within a reef ecosystem as a platform for the evaluation of recreational angling management strategies. Angler behaviour is described using econometrically estimated site choice models. Site choice among anglers is driven by site attributes and angler characteristics. The biophysical model represents the marine reef environment as a system with different trophic levels representing algal and coral growth as well as piscivores and herbivores fish. Ecosystem dynamics are driven by interactions within the trophic levels and fishing activities. The model is capable of simulating the biophysical and economic welfare impacts of management strategies in a manner that accounts for feedback effects.
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Paper Nr: 255
Title:

EVALUATING JASON FOR DISTRIBUTED CROWD SIMULATIONS
Authors:

Victor Fernández-Bauset, Francisco Grimaldo, Miguel Lozano and Juan Manuel Orduña
Abstract: Large-scale crowd simulations require distributed computer architectures and efficient parallel techniques to achieve the rendering of visually plausible images while simulating the behaviour of crowds of autonomous agents. The Java-based multiagent platforms, devoted to provide the agents with the required lifecycle, represent a key middleware in crowd systems. However, since they are oriented to maximize portability and to reduce the development cost, they may reduce performance and scalability, two important requirements in large-scale crowd simulation systems. This paper studies the performance and scalability provided by Jason, a well known Java-based BDI-MAS platform, as a plausible framework to be used for large-scale crowd simulations. The performance evaluation results show that some improvements should be performed in order to make Jason a suitable middleware for large-scale crowd simulations.
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Paper Nr: 284
Title:

A contextual environment approach for multi-agent-based simulation
Authors:

Fabien Badeig, Flavien Balbo and Suzanne Pinson
Abstract: Multi-agent-based simulation (MABS) is used to understand complex real life processes and to experiment several scenarios in order to reproduce, understand and evaluate these processes. A crucial point in the design of a multi-agent-based simulation is the choice of a scheduling policy. In classical multi-agent-based simulation frameworks, a pitfall is the fact that the action phase, based on local agent context analysis, is repeated in each agent at each time cycle during the simulation execution. This analysis inside the agents reduces agent flexibility and genericity and limits agent behavior reuse in various simulations. If the designer wants to modify the way the agent reacts to the context, he could not do it without altering the way the agent is implemented because the link between agent context and agent actions is an internal part of the agent. In contrast to classical approaches, our proposition, called EASS (Environment as Active Support for Simulation), is a new multi-agent-based simulation framework, where the context is analyzed by the environment and where agent activation is based on context evaluation. This activation process is what we call contextual activation. The main advantage of contextual activation is the improvement of complex agent simulation design in terms of flexibility, genericity and agent behavior reuse.
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Paper Nr: 290
Title:

COORDINATING AGENTS: An analysis of coordination in supply-chain management like tasks
Authors:

Chetan Yadati, Cees Witteveen and Yingqian Zhang
Abstract: A multi-agent planning problem consists of a set of activities that need to be planned by several agents. Here, plan coordination methods play an important role, since the independently generated plans by different agents can lead to an infeasible joint solution. We study one particular plan coordination approach, called coordination-by-design, which allows each agent to make its own plan completely independent of the others, while guaranteeing the feasibility of the combined plan of all the agents as a joint solution to the multi-agent planning problem. In this paper, we are interested in a class of multi-agent planning problems that arise in supply-chain management applications. Although the coordination problem in general is $\Sigma^p_2$-complete, it turns out for this special class, the complexity of coordination checking is polynomial and deciding a minimum coordination set is NP-complete. We develop a polynomial-time approximation algorithm to compute a sufficient coordination set.
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Paper Nr: 304
Title:

CONEMAF: A Modular Multi Agent Framework for Autonomic Network Management
Authors:

Julien Boite, Gérard Nguengang, Maurice Israel and Vania Conan
Abstract: Communication networks are growing both in terms of size and complexity. Because of the huge amount of monitored data to analyse and correlate, the management task that relies mostly on human operators is becoming time-consuming, labour-intensive and costly. The centralized management paradigm adopted by current management systems is no longer suitable for such networks. A distributed management with more autonomy delegated to network devices is therefore paramount to master this complexity efficiently. Multi-agent systems characteristics fit the requirements that must be taken into account to integrate autonomicity in networks. This paper presents CONEMAF, a novel modular multi-agent platform for autonomic network management. CONEMAF is fully distributed, allows situated knowledge analysis and implements a cognitive cycle for autonomic management. Implemented on top of OSGi, the present release makes use of well-proven Java-based COTS. CONEMAF has been deployed on Linux routers and is demonstrated for autonomic routing in a wireless mesh network.
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Paper Nr: 310
Title:

An agent based simulation of the dynamics in cognitive depressogenic thought
Authors:

Michel Klein, Azizi A. Aziz and Michel Klein
Abstract: Depression is a common mental disorder. Appropriate support from others can reduce the cognitive distortion that can be caused by subsequent depressions. To increase our understanding of this process, a multi-agent model is presented in this paper in which the positive and negative effects of social support and its relation with cognitive thoughts are modelled. Simulations show the effect of social support on different personality types. A mathematical analysis of the stable situations in the model gives an additional explanation of extreme cases. Finally, a formal verification of expected relations between support, risk factors and depressive thoughts is performed on the simulation traces to check whether the simulations describe realistic processes.
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Paper Nr: 35
Title:

Map exploration using a line-based formation of mobile robots
Authors:

Bart Wyns, Jens Boeykens and Luc Boullart
Abstract: Exploration of an unknown environment is a well-studied problem for single robot systems. However, using just a single robot limits the speed in which a map can be fully explored. Using a multi-robot approach, a noticeable performance gain can be achieved. In this article a line-based formation strategy to explore a static area is introduced, without making any assumptions about the shape of the obstacles within. A software simulator including this line-based formation strategy was built to evaluate performance in different environments. Results show that the robot formation can easily get around both convex and concave obstacles whilst constructing a map that is both complete and correct.
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Paper Nr: 41
Title:

SAM : Semantic Agent Model for SWRL rule-based agents
Authors:

Julien Subercaze and Pierre Maret
Abstract: SemanticWeb technologies are part of multi-agent engineering, especially regarding knowledge base support. Recent advances in the field of logic for the semantic web enable a new range of applications. Among them, programming agents based on semantic rules is a promising field. In this paper we present an semantic agent model that allows SWRL programming of agents. Our approach based on the extended finite state machine concept results in a three layers architecture. We detail the architecture, the syntax of the rules , the agent interpreter cycle and present a prototype validating the concept. We present two distinguished features of our approach : behavior exchanges and consistency checking.
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Paper Nr: 50
Title:

USING AGENTS TO CONFRONT SOME OF THE CHALLENGES OF KNOWLEDGE MANAGEMENT SYSTEMS
Authors:

Javier Portillo, Aurora Vizcaino, Juan Pablo Soto and Mario Piattini
Abstract: The importance of knowledge management has, in recent years, led to the incorporation of Knowledge Management Systems (KMS) into companies. Some of these KMS could be considered as Recommender Systems that are able to recommend knowledge, which is part of the company’s intellectual capital. However, these KMS are not always welcome in the company, since the knowledge is not stored by using a quality control, or because employees feel that these kinds of systems, rather then helping them, cause them extra work. In this paper we present an agent architecture combined with a trust model trying to avoid some of the problems that appear when a KMS is introduced into companies.
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Paper Nr: 56
Title:

MANAGING COMBINATORIAL OPTIMIZATION PROBLEMS BY MEANS OF EVOLUTIONARY COMPUTATION AND MULTI-AGENT SYSTEM
Authors:

Mauricio Paletta, Mauricio Paletta and Pilar Herrero
Abstract: The necessity for solving a combinatorial optimization problem is very common. Evolutionary/genetic program could be used to deal with such situations. Unfortunately, depending on the complexity of the problem, high computational capabilities are required, primarily in those cases in which measuring the quality of a potential solution is very demanding. However, advances in Distributed Artificial Intelligence (DAI), Multi-Agent Systems (MAS) to be more specific, could help users to deal with this situation by parallelizing the evolutionary program aiming to distribute the computational capabilities required. This paper presents an inter-agent MAS protocol for parallelizing an evolutionary program aiming to reduce the communications requirements necessary as well as allowing a response within a reasonable period of time
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Paper Nr: 60
Title:

Combining self-motivation with logical planning and inference in a reward-seeking agent
Authors:

Daphne Liu, Daphne Liu and Lenhart Schubert
Abstract: We present our preliminary work on our framework for building self-motivated, self-aware agents that plan continuously so as to maximize long-term rewards. While such agents employ reasoned exploration of feasible sequences of actions and corresponding states, they also behave opportunistically and recover from failure, thanks to their quest for rewards and their continual plan updates. The framework allows for both specific and general (quantified) knowledge, epistemic predicates such as {\it knowing-that} and {\it knowing-whether}, for incomplete knowledge of the world, for quantitative change, for exogenous events, and for dialogue actions. Question answering and experimental runs are shown for a particular agent $ME$ in a simple world of roads, various objects, and another agent, demonstrating the value of continual, deliberate, reward-driven planning.
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Paper Nr: 72
Title:

Formal model to integrate Multi-Agent Systems and Interactive Graphic Systems
Authors:

Antonio-Javier Gallego-Sánchez, Rafael Molina and Gabriel López-García
Abstract: A formal grammar-based model is presented to integrate the essential characteristics of a Multi-Agent System with the visualization given by an Interactive Graphic Systems. This model adds several advantages, such as the separation between the implementation of the system activity and the hardware devices, or the easy reusability of components. To illustrate the model, a practical case is presented. It implements a simulation of the fires in forests caused by lightning.
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Paper Nr: 102
Title:

Involving Web-Trading Agents & MAS: An implementation for searching and recovering environmental information
Authors:

L. Iribarne, Luis Iribarne, Nicolás Padilla Soriano, José A. Asensio Cortés, Francisco Muñoz Berenguel and Javier Criado Rodríguez
Abstract: The Web-based Information Systems appear to facilitate the access of user(s) to different kind of informa-tion geographically distributed in different regions (both data and users). In a web system, we have the pos-sibility to use components called traders that improve the interoperability with other agents or even others trading systems. This paper describes details for a Web Trading Agent in a Multi-Agent System, which implements a distributed information system, called SOLERES. Besides, it presents the communica-tion system based on ontologies through which the system agents communicate with each other and with the trading agent inside the SOLERES system (i.e., an Environmental Management System). This architecture is based on a “Query-Searching/Recovering-Response” model. For the implementation it uses: an interface user agent and the trader agent, “processes” ontologies for the agent communication, “data” ontologies for the information storage, SPARQL notation for queries and the JADE platform for the implementation.
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Paper Nr: 112
Title:

LEARNING ACTION SELECTION STRATEGIES IN COMPLEX SOCIAL SYSTEMS
Authors:

Marco Remondino, Anna M. Bruno and Nicola Miglietta
Abstract: In this work, a new method for cognitive action selection is formally introduced, keeping into consideration an individual bias for the agents: ego biased learning. It allows the agents to adapt their behaviour according to a payoff coming from the action they performed at time t-1, by converting an action pattern into a synthetic value, updated at each time, but keeping into account their individual preferences towards specific actions. In agent based simulations, the many entities involved usually deal with an action selection based on the reactive paradigm: they usually feature embedded strategies to be used according to the stimuli coming from the environment or other entities. The actors involved in real Social Systems have a local vision and usually can only see their own actions or neighbours’ ones (bounded rationality) and sometimes they could be biased towards a particular behaviour, even if not optimal for a certain situation. Some simulations are run, in order to show the effects of biases, when dealing with an heterogeneous population of agents.
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Paper Nr: 150
Title:

DYNAMIC SERVICE DISCRIMINATION STRATEGY DEVELOPMENT USING GAME THEORY
Authors:

Kwang S. Shin, KwangSup Shin, Jae-Yoon Jung, Suk-Ho Kang and Doug Y. Suh
Abstract: This research proposes a dynamic service discrimination strategy for wireless multimedia services. In particular, bargaining solutions in the game theory are applied to allocate the limited resources to users for the purpose of proportional fairness. We assume that users can choose one of discriminated media services and multimedia resources are then allocated to users according to their service selections. In the mechanism, an efficiency function for the network manager and a utility function for users are devised to reflect quality of service and cost. The optimized service discrimination and resource allocation policy have been developed not only from user’s standpoint, but also from network manager’s. We illustrated experimental results with synthesis multimedia data and analyzed the effect of the proposed service differentiation and resource allocation algorithms.
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Paper Nr: 154
Title:

PROGRAMMING REACTIVE AGENT-BASED MOBILE ROBOTS USING ICARO-T FRAMEWORK
Authors:

José M. Gascueña, Antonio Fernández-Caballero and Francisco J. Garijo
Abstract: This paper describes the experience and the results of using agent-based component patterns for developing mobile robots. The work is based on the open source ICARO-T framework, which provides four categories of component patterns: agent organization pattern to describe the overall architecture of the system, cognitive and reactive agent patterns to model agent behaviour, and resource patterns to encapsulate computing entities providing services to agents. The experimental setting is based on the development of a team of cooperating robots for achieving surveillance tasks. The development approach illustrated with working examples of the utilization of the patterns are detailed in the paper. Advantages for cooperative robot systems are drawn in the conclusions.
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Paper Nr: 168
Title:

A GENERIC COGNITIVE SYSTEM ARCHITECTURE APPLIED TO THE UAV FLIGHT GUIDANCE DOMAIN
Authors:

Stefan Brueggenwirth, Alexander Matzner, Ruben Strenzke and Axel Schulte
Abstract: We present an overview of our cognitive system architecture (COSA) with applications in the multi-UAV flight guidance and mission management areas. Our work is based on a modified version of the psychological Rasmussen scheme. We belief that modeling in close analogy with categories of human behavior simplifies human-machine interaction as well as the knowledge engineering process. Accordingly, our hybrid agent architecture is comprised of a low-level, reactive layer with prestored procedures and a goal-oriented, deliberative layer that enables inference and dynamic planning. The first, fully functional version of our architecture is based purely on production rules and the Soar interpreter, enhanced with syntax extensions specific to our modeling approach. We then developed our own inference machine based on graph matching which natively support extensions such as type-safety and class-inheritance and resulted in performance improvements over the original Rete algorithm of Soar. A major weakness of our current implementation still lies in its static planning functionality which is realized by a means-ends plan library. We discuss a concept that interleaves the planning process with knowledge about anticipated action outcomes, followed by an interpretation of projected future world states with respect to current goals. We illustrate this principle with a multi-UAV scenario.
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Paper Nr: 171
Title:

MULTIAGENT SYSTEM FOR THE PREVENTION OF ACCIDENTS OF PEOPLE LIVING ALONE
Authors:

Miguel A. Sanz-Bobi, David Contreras, Javier Garcia, Alberto Perez and José J. de Vicente
Abstract: This paper describes a multiagent system designed for assisting elderly and disabled people living alone in their homes. The main objective of this system is to prevent risky situations by monitoring key variables of daily life. This system is conceived as pro-active advancing warnings issued to the user, but also, if necessary, to the caretaker and the corresponding assistance services. This multi-agent system has been designed to be easily extended and adapted to different user requirements. The agents have different roles related to the acquisition of information, the processing of it and communication to the different human agents.
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Paper Nr: 197
Title:

Archetype-based Semantic Interoperability in Healthcare
Authors:

José Machado, José A. Marques, António Correia, José Neves and Lúcia Cerqueira
Abstract: Advances in new Methodologies for Problem Solving and Information Technology enable a fundamental redesign of health care processes based on the use and integration of data and/or knowledge at all levels, in a healthcare environment. Indeed, new communication technologies may support a transition from institution centric to patient-centric based applications, i.e., the health care system is faced with a series of challenges, namely those concerning quality-of-information and the cost-effectiveness of such processes. The distribution of cost-effective health care allowing the patient to take active part in the caring process, provision of evidence-based care on all levels in the system and effective use and reuse of information are key issues for the health care organization. The information and communication technology infrastructure should therefore reflect the view of the health care system as a seamless system where information can flow across organizational and professional borders. Therefore, in this work it will be address key principles that must be at the center of patient-centered technologies for disease management and prevention, namely those referred to above.
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Paper Nr: 202
Title:

Agency Services: An Agent-Based and Services-Oriented Model for Building Large Virtual Communities
Authors:

Ignacio Lopez Rodriguez and Mario Hernandez Tejera
Abstract: Despite the current importance of internet and the increasing appearance of virtual societies, there is still no widespread adoption of intelligent agents. Building solutions based on intelligent agents is not a simple task because of the complex nature of the problems that they face, which are mainly related to planning, cooperation and negotiation tasks. In order to overcome those difficulties, and in line with the recent success of the Cloud Computing model, this paper proposes a new model in which brokers able to represent users in virtual societies are offered as one more service of the cloud. The paper also details the technologies necessary to construct a realistic solution which, apart from abstracting the user from all the details of the implementation, loses none of the characteristic advantages of solutions based on agents and Cloud Computing.
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Paper Nr: 203
Title:

A mobile intelligent synthetic character with natural behavior generation
Authors:

Jongwon Yoon and Sung-Bae Cho
Abstract: As cell phones have become essential tools for human communication and especially smartphones rise as suitable devices to implement ubiquitous computing, personalized intelligent services in smartphones are required. There are many researches to implement services, and an intelligent synthetic character is one of them. This paper proposes a structure of emotional intelligent synthetic character which generates natural and flexible behaviour in various situations. In order to generate the character’s more natural behaviour, we used the Bayesian networks to infer the user’s states and we used OCC model to create the character’s emotion. After inferring these information, the behaviours are generated through the behaviour networks with using the information. Moreover, we organized a usability test to verify a usability of the proposed structure of the character.
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Paper Nr: 225
Title:

AN AGENT FRAMEWORK FOR PERSONALISED STUDENT SELF-EVALUATION
Authors:

María T. París Regueiro and Mariano Cabrero
Abstract: The European Higher Education Area, an agreement by 29 countries to unite and harmonise qualifications and Universities’ rapprochement to the real demands of the labour market, will make a significant change in the traditional model of teaching. The lecturer will have to adapt his methods, techniques and teaching tools to carry out more personalised monitoring of the student’s work, leading to the possibility of continuous evaluation. The suitable use of ICT can make a contribution to improving the quality of teaching and learning. In this context, a self evaluation platform is developed using the technology of Intelligent Agents. This system can be adaptable as it adjusts the various self evaluation tests to the student’s level of knowledge. Each student has a profile and, depending on this, timing and interaction is set by the agents.
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Paper Nr: 239
Title:

A Tool Environment for Specifying and Verifying Multi-Agent Systems

Authors:

Christian Schwarz, Ammar Mohammed and Frieder Stolzenburg
Abstract: We present a tool environment with a constraint logic programming core, that allows us to specify multi-agent systems graphically and verify them automatically. This combines the advantages of graphical notations from software engineering and formal methods. We demonstrate this on a Robocup rescue scenario.
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Paper Nr: 245
Title:

HIERARCHICAL COORDINATION : Towards Scheme based on problem spliting

Authors:

Said Brahimi, Ramdane Maamri and Zaidi Sahnoun
Abstract: using multi-agent planning in real and complex environments requires using a flexible coordination scheme. The aim of this paper is to give a principle of coordination scheme for systems that work in these environments. This scheme is viewed as a hierarchical structure of coordination cells (CC). Each cell is controlled by meta-level agent, and is occupied to coordinating a sub-set of plans. The structure of coordination scheme, that is dynamically formed, can be purely centralized, purely distributed, or hierarchical according to interdependency degree of plans. The idea, behind of, is based on problem splitting techniques. This technique that is embodied in the coordination process, allows to reorganizing structure of CC. there are two mains operation on CC: split and merge. Each CC will be split if the problem of coordination can be divided. The CCs should be merged according failure of a cell to find a solution.
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Paper Nr: 308
Title:

KNOWLEDGE REPRESENTATION : An Ontology for Managing a Virtual Environment
Authors:

Lydie EDWARD, Kahina Amokrane, Jean-Paul Barthes and Domitile Lourdeaux
Abstract: This paper presents an ontology developed in order to manage a virtual environment for risk prevention. This ontology represents the objects composing the environment, the agents operating in the environment and the events that can happened. In the virtual environment, different entities cohabit: virtual operators represented by cognitive agents and the learner's avatar that represents a real operator. They can interact with the objects. It is therefore useful to have on one hand a managing system that well define the framework in which the interactions or actions can be allowed and on the other hand a representation of the knowledge involve in such interactions. To do this, we combine artificial intelligence and knowledge engineering to propose agent COLOMBO. It is composed with the developed ontology and a set of reasoning rules.
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Paper Nr: 323
Title:

Multi agents system on epileptic network
Authors:

Abel Kinie
Abstract: This work is focused on the study and interpretation of epileptic signals, based on the analysis of stereo electroencephalographic (SEEG) signals with signal processing method and multi-agent approach. The objective is to use this technique to improve information extraction, representation and interpretation as well as the implemented control strategies in the different processes. Our approach deals with the information recorded during the intercerebral exploration and it exploits a dynamic selection of the interest’s information to optimize the processing without truncating the information. We associated signal processing algorithms (spectrum analysis, causality measure between signals) approved in the analysis of the epileptic signal in a multi-agent system.
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Paper Nr: 349
Title:

TOWARDS ROBUST HYBRID CENTRAL/SELF-ORGANIZING MULTI-AGENT SYSTEMS
Authors:

Yaser Chaaban, Jörg Hähner and Christian Müller-Schloer
Abstract: The Organic Computing initiative uses life-like properties such as self-organisation, self-optimisation and self-configuration towards building today’s technical systems as flexible, robust, and adaptive systems. In a previous paper, we proposed a system for coordinating semi-autonomous agents under the framework of Organic Computing. It uses abstractions of observer and controller to add robustness and solve scheduling/allocation problems. In this context, the path planning and the observation of the agents were presented and also the detection of deviations in different situations was discussed. In this paper, we introduce control features of the system designed to deal with these types of deviations. That leads in turn to intervene in time when it is necessary, so that the system remains demonstrating robustness. Furthermore, this paper addresses the conflict between a central planning algorithm and the autonomy of the agents. A hybrid central/self-organizing multi-agent system is introduced solving this conflict.
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Paper Nr: 353
Title:

COORDINATION IN OPEN AND UNSTRUCTURED INTELLIGENT AGENT SOCIETIES Using distributed planners on top of a semantic overlay network
Authors:

António Lopes and Luis Botelho
Abstract: Collaborative environments, where multiple heterogeneous agents (managing several resources) can coop- erate in pursuing common and individual goals, are a step forward in creating real-world agent societies. However, current research in agent negotiation and in service coordination is still not enough for building such an agent-based society, capable of jointly solving complex planning problems and still achieve overall good performance. Most often, current work relies on either some centralised component or pre-defined so- cial structure, which can compromise the system in terms of scalability, openness and robustness, and fails to address general problems. By using efficient network search algorithms and network evolution techniques it is possible to build and maintain a semantic overlay network from a totally unstructured distributed network, which in turn will simplify and optimize the distributed planning process amongst heterogeneous agents. We developed distributed versions of well-known planners and through a set of tests (using different scenarios) we were able to determine which algorithms are best for each situation. Hence, by combining the semantic resource discovery process with the appropriate distributed planner, it is possible to build a scalable, robust and efficient agent society capable of solving complex distributable problems.
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Paper Nr: 374
Title:

An Approach to Personalisation in E-learning Social Environments

Authors:

Hend Ben Hadji and Hend Ben Hadji
Abstract: We present an approach to a personalized learning service that takes advantage of social tagging to support social learning in context and provides learning resources adapted to the abilities and needs of an individual learner. We employ graph clustering technique in order to group tags into clusters having different contexts, learner’s personomy to discover the learning context of the targeted learner, and Formal Concept hierarchy theory to hierarchically cluster learning resources and retrieve the relevant resources to the learner’s needs.
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