ICAART 2016 Abstracts

Area 1 - Artificial Intelligence

Full Papers

Paper Nr:	3
Title:	Capturing Graded Knowledge and Uncertainty in a Modalized Fragment of OWL
Authors:	Hans-Ulrich Krieger
Abstract:	Natural language statements uttered in diagnosis (e.g., in medicine), but more general in daily life are usually graded, i.e., are associated with a degree of uncertainty about the validity of an assessment and is often expressed through specific verbs, adverbs, or adjectives in natural language. In this paper, we look into a representation of such graded statements by presenting a simple non-standard modal logic which comes with a set of modal operators, directly associated with the words indicating the uncertainty and interpreted through confidence intervals in the model theory. We complement the model theory by a set of RDFS-/OWL 2 RL-like entailment (if-then) rules, acting on the syntactic representation of modalized statements. Our interest in such a formalization is related to the use of OWL as the de facto language in today’s ontologies and its weakness to represent and reason about assertional knowledge that is uncertain or that changes over time.
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Paper Nr:	35
Title:	A Multi-context Framework for Modeling an Agent-based Recommender System
Authors:	Amel Ben Othmane, Andrea Tettamanzi, Serena Villata, Nhan Le Thanh and Michel Buffa
Abstract:	In this paper, we propose a multi-agent recommender system based on the Belief-Desire-Intention (BDI) model applied to multi-context systems. First, we extend the BDI model with additional contexts to deal with sociality and information uncertainty. Second, we propose an ontological representation of planning and intention contexts in order to reason about plans and intentions. Moreover, we show a simple real-world scenario in healthcare in order to illustrate the overall reasoning process of our model.
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Paper Nr:	41
Title:	Modeling Uncertainty in Support Vector Surrogates of Distributed Energy Resources - Enabling Robust Smart Grid Scheduling
Authors:	Jörg Bremer and Sebastian Lehnhoff
Abstract:	Robust proactive planning of day-ahead real power provision must incorporate uncertainty in feasibility when trading off different schedules against each other during the predictive planning phase. Imponderabilities like weather, user interaction, projected heat demand, and many more have a major impact on feasibility – in the sense of being technically operable by a specific energy unit. Deviations from the predicted initial operational state of an energy unit may easily foil a planned schedule commitment and provoke the need for ancillary services. In order to minimize control power and cost arising from deviations from agreed energy product delivery, it is advantageous to a priori know about individual uncertainty. We extend an existing surrogate model that has been successfully used in energy management for checking feasibility during constraint-based optimization. The surrogate is extended to incorporate confidence scores based on expected feasibility under changed operational conditions. We demonstrate the superiority of the new surrogate model by results from several simulation studies.
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Paper Nr:	45
Title:	Simultaneous Scheduling of Machines and a Single Moving Robot in a Job Shop Environment by Metaheuristics based Clustered Holonic Multiagent Model
Authors:	Houssem Eddine Nouri, Olfa Belkahla Driss and Khaled Ghédira
Abstract:	In systems based robotic cells, the control of some elements such as transport robot has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and a Single Robot (JSPT-SR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by a single transport robot. Hence, the JSPT-SR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-SR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using benchmark data instances from the literature of JSPT-SR. New upper bounds are found, showing the effectiveness of the presented approach.
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Paper Nr:	46
Title:	Learning User Preferences in Matching for Ridesharing
Authors:	Mojtaba Montazery and Nic Wilson
Abstract:	Sharing car journeys can be very beneficial, since it can save travel costs, as well as reducing traffic congestion and pollution. The process of matching riders and drivers automatically at short notice, is referred to as dynamic ridesharing, which has attracted a lot of attention in recent years. In this paper, amongst the wide range of challenges in dynamic ridesharing, we consider the problem of ride-matching. While existing studies mainly consider fixed assignments of participants in the matching process, our main contribution is focused on the learning of the user preferences regarding the desirability of a choice of matching; this could then form an important component of a system that can generate robust matchings that maintain high user satisfaction, thus encouraging repeat usage of the system. An SVM inspired method is exploited which is able to learn a scoring function from a set of preferences; this function measures the predicted satisfaction degree of the user regarding specific matches. To the best of our knowledge, we are the first to present a model that is able to implicitly learn individual preferences of participants. Our experimental results, which are conducted on a real ridesharing data set, show the effectiveness of our approach.
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Paper Nr:	48
Title:	Interaction Patterns in Computer-assisted Semantic Annotation of Text - An Empirical Evaluation
Authors:	Jaroslav Dytrych and Pavel Smrz
Abstract:	This paper examines user interface options and interaction patterns evinced in tools for computer-assisted semantic enrichment of text. It focuses on advanced annotation tasks such as hierarchical annotation of complex relations and linking entities with highly ambiguous names and explores how decisions on particular aspects of annotation interfaces influence the speed and the quality of computer-assisted human annotation processes. Reported experiments compare the 4A annotation system, designed and implemented by our team, to RDFaCE and GATE tools that all provide advanced annotation functionality. Results show that users are able to reach better consistency of event annotations in less time when using the 4A editor. A set of experiments is then conducted that employ 4A’s high flexibility and customizability to find an optimal amount of displayed information and its presentation form to reach best results in linking entities with highly ambiguous names. The last set of experiments then proves that 4A’s particular way of implementing the concept of semantic filtering speeds up event annotation processes and brings higher consistency when compared to alternative approaches.
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Paper Nr:	52
Title:	Abstract Dialectical Frameworks for Text Exploration
Authors:	Elena Cabrio and Serena Villata
Abstract:	Textual Entailment (TE) systems aim at recognizing the relations of entailment or non entailment holding between two text fragments (i.e. a pair). The identified TE pairs are considered as independent one from the others. However, in the latest years TE systems have been challenged against a number of real world application scenarios like analyzing costumers interactions about a service, or analyzing online debates. These applications have underlined the need to move from TE pairs to TE graphs where pairs are no more independent. Moving from single pairs to graphs has the advantage of providing an overall view of the topic discussed in the text. The challenge here is to define ways to exploit such graph-based representation for text exploration. In the literature, some approaches apply abstract argumentation theory to compute the accepted arguments of a debate, but they present a number of drawbacks, e.g., the non entailment relation and the attack relation in abstract argumentation are assumed to be equivalent, but this is not always the case. In this paper, we define bipolar entailment graphs, i.e., graphs whose nodes are text fragments and the edges represent the entailment or non entailment relations. We adopt abstract dialectical frameworks to define acceptance conditions for the nodes such that the resulting framework returns us relevant information for our text exploration task. Experimental evaluation shows the feasibility of our approach.
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Paper Nr:	54
Title:	Improving Cascade Classifier Precision by Instance Selection and Outlier Generation
Authors:	Judith Neugebauer, Oliver Kramer and Michael Sonnenschein
Abstract:	Beside the curse of dimensionality and imbalanced classes, unfavorable data distributions can hamper classification accuracy. This is particularly problematic with increasing dimensionality of the classification task. A classifier that can handle high-dimensional and imbalanced data sets is the cascade classification method for time series. The cascade classifier can compound unfavorable data distributions by projecting the high-dimensional data set onto low-dimensional subsets. A classifier is trained for each of the low-dimensional data subsets and their predictions are aggregated to an overall result. For the cascade classifier, the errors of each classifier accumulate in the overall result and therefore small improvements in each small classifier can improve the classification accuracy. Therefore we propose two methods for data preprocessing to improve the cascade classifier. The first method is instance selection, a technique to select representative examples for the classification task. Furthermore, artificial infeasible examples can improve classification performance. Even if high-dimensional infeasible examples are available, their projection to low-dimensional space is not possible due to projection errors. We propose a second data preprocessing method for generating artificial infeasible examples in low-dimensional space. We show for micro Combined Heat and Power plant power production time series and an artificial and complex data set that the proposed data preprocessing methods increase the performance of the cascade classifier by increasing the selectivity of the learned decision boundaries.
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Paper Nr:	57
Title:	Evolution Strategies and Covariance Matrix Adaptation - Investigating New Shrinkage Techniques
Authors:	Silja Meyer-Nieberg and Erik Kropat
Abstract:	This paper discusses the covariance matrix adaptation in evolution strategies, a central and essential mechanism for the search process. Basing the estimation of the covariance matrix on small samples w.r.t. the search space dimension is known to be problematic. However, this situation is common in optimization raising the question, whether the performance of the evolutionary algorithms could be improved. In statistics, several approaches have been developed recently to improve the quality of the maximum-likelihood estimate. However, they are seldom applied in evolutionary computation. Here, we focus on linear shrinkage which requires relatively little additional effort. Several approaches and shrinkage targets are integrated into evolution strategies and analyzed in a series of experiments.
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Paper Nr:	58
Title:	On the Decomposition of Min-based Possibilistic Influence Diagrams
Authors:	Salem Benferhat, Hadja Faiza Khellaf-Haned and Ismahane Zeddigha
Abstract:	Min-based possibilistic influence diagrams allow a compact modelling of decision problems under uncertainty. Uncertainty and preferential relations are expressed on the same structure by using ordinal data. Like probabilistic influence diagrams, min-based possibilistic influence diagrams contain three types of nodes: chance, decision and utility nodes. Uncertainty is described by means of possibility distributions on chance nodes and preferences are expressed as satisfaction degrees on utility nodes. In many applications, it may be natural to represent expert knowledge and preferences separately and treat all nodes similarly. This paper shows how an influence diagram can be equivalently represented by two possibilistic networks: the first one represents knowledge of an agent and the second one represents agent’s preferences. Thus, the decision evaluation process is based on more compact possibilistic network.
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Paper Nr:	61
Title:	AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization
Authors:	Gerasimos Spanakis and Gerhard Weiss
Abstract:	Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to facilitate visualization). Neurons in the output space are connected with each other but this structure remains fixed throughout training and learning is achieved through the updating of neuron reference vectors in feature space. Despite the fact that growing variants of SOM overcome the fixed structure limitation they increase computational cost and also do not allow the removal of a neuron after its introduction. In this paper, a variant of SOM is proposed called AMSOM (Adaptive Moving Self-Organizing Map) that on the one hand creates a more flexible structure where neuron positions are dynamically altered during training and on the other hand tackles the drawback of having a predefined grid by allowing neuron addition and/or removal during training. Experiments using multiple literature datasets show that the proposed method improves training performance of SOM, leads to a better visualization of the input dataset and provides a framework for determining the optimal number and structure of neurons.
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Paper Nr:	67
Title:	Multi-Agent Plan Recognition as Planning (MAPRAP)
Authors:	Chris Argenta and Jon Doyle
Abstract:	A key challenge in Multi-agent Plan Recognition (MPAR) is effectively pruning the large search space of potential goal / team compositions because multi-agent scenarios distribute actions/observables across agents. This additional dimension also makes creating a priori plan libraries difficult. In this paper, we describe our strategy for discrete Multi-agent Plan Recognition as Planning (MAPRAP), which extends Ramirez and Geffner’s Plan Recognition as Planning (PRAP) approach into multi-agent domains. MAPRAP (like PRAP) uses a planning domain (not a library) to synthesize and compare utility costs of plan instances that incorporate potential goals and previous observables to identify the plan being carried out by teams of agents. This initial discrete implementation of MAPRAP includes two pruning strategies to address the explosion of hypotheses. We establish a performance profile for discrete MAPRAP using the well-known multi-agent blocks-world benchmark domain. We varied the number of teams, agent count, and goal sizes. We measured accuracy, precision, and recall at each time step. For pruning efficiency, we compare two strategies. In the more aggressive case our multi-agent team blocks scenarios averaged 1.05 plans synthesized per goal per time step (compared to 0.56 for single agent scenarios) demonstrating feasibility of MAPRAP and benchmarking for future improvements.
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Paper Nr:	73
Title:	Secrecy-Preserving Query Answering in ELH Knowledge Bases
Authors:	Gopalakrishnan Krishnaswamy Sivaprakasam and Giora Slutzki
Abstract:	In this paper we study Secrecy-Preserving Query Answering problem under Open World Assumption (OWA) for ELH Knowledge Bases (KBs). We employ two tableau procedures designed to compute some consequences of ABox (A) and TBox (T ) denoted by A∗ and T ∗ respectively. A secrecy set of a querying agent is subset S of A∗ ∪ T ∗ which the agent is not allowed to access. An envelope is a superset of the secrecy set which provides logical protection to the secrecy set against the reasoning of the querying agent. Once envelopes are computed, they are used to efficiently answer assertional and GCI queries without compromising the secret information in S. Answering GCI queries while preserving secrecy has not been studied in the current literature. When the querying agent asks a query q, the reasoner answers “Yes” if KB \|= q and q does not belong to the envelopes; otherwise, the reasoner answers “Unknown”. Being able to answer “Unknown” plays a key role in protecting secrecy under OWA. Since we are not computing all the consequences of the KB, answers to the queries based on just A∗ and T ∗ could be erroneous. To fix this problem, we further augment our algorithms to make the query answering procedure foolproof.
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Paper Nr:	94
Title:	Automatic Generation of Fuzzy Membership Functions using Adaptive Mean-shift and Robust Statistics
Authors:	Hossein Pazhoumand-Dar, Chiou-Peng Lam and Martin Masek
Abstract:	In this paper, an unsupervised approach incorporating variable bandwidth mean-shift and robust statistics is presented for generating fuzzy membership functions from data. The approach takes an attribute and automatically learns the number of representative functions from the underlying data distribution. Given a specific membership function, the approach also works out the associated parameters. The investigation here examines the application of approach using the triangular membership function. Results from partitioning of attributes confirm that the generated membership functions can better separate the underlying distributions when compared to a number of other techniques. Classification performance of fuzzy rule sets produced using four different methods of parameterizing the associated attributes is examined. We observed that the classifier constructed using the proposed method of generating membership function outperformed the 3 other classifiers that had used other methods of parameterizing the attributes.
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Paper Nr:	100
Title:	Promoting Cooperation and Fairness in Self-interested Multi-Agent Systems
Authors:	Ted Scully and Michael G. Madden
Abstract:	The issue of collaboration amongst agents in a multi-agent system (MAS) represents a challenging research problem. In this paper we focus on a form of cooperation known as coalition formation. The problem we consider is how to facilitate the formation of a coalition in a competitive marketplace, where self-interested agents must cooperate by forming a coalition in order to complete a task. Agents must reach a consensus on both the monetary amount to charge for completion of a task as well as the distribution of the required workload. The problem is further complicated because different subtasks have various degrees of difficulty and each agent is uncertain of the payment another agent requires for performing specific subtasks. These complexities, coupled with the self-interested nature of agents, can inhibit or even prevent the formation of coalitions in such a real-world setting. As a solution, an auction-based protocol called ACCORD is proposed. ACCORD manages real-world complexities by promoting the adoption of cooperative behaviour amongst agents. Through extensive empirical analysis we analyse the ACCORD protocol and demonstrate that cooperative and fair behaviour is dominant and any agents deviating from this behaviour perform less well over time.
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Paper Nr:	101
Title:	Recursive Reductions of Internal Dependencies in Multiagent Planning
Authors:	Jan Tožička, Jan Jakubův and Antonín Komenda
Abstract:	Problems of cooperative multiagent planning in deterministic environments can be efficiently solved both by distributed search or coordination of local plans. In the current coordination approaches, behavior of other agents is modeled as public external projections of their actions. The agent does not require any additional information from the other agents, that is the planning process ignores any dependencies of the projected actions possibly caused by sequences of other agents’ private actions. In this work, we formally define several types of internal dependencies of multiagent planning problems and provide an algorithmic approach how to extract the internally dependent actions during multiagent planning. We show how to take an advantage of the computed dependencies by means of reducing the multiagent planning problems. We experimentally show strong reduction of majority of standard multiagent benchmarks and nearly doubling of solved problems in comparison to a variant of a planner without the reductions. The efficiency of the method is demonstrated by winning in a recent competition of distributed multiagent planners.
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Paper Nr:	118
Title:	A Comparative Study of Programming Agents in POSH and GOAL
Authors:	Rien Korstanje, Cyril Brom, Jakub Gemrot and Koen V. Hindriks
Abstract:	A variety of agent programming languages have been proposed but only few comparative studies have been performed to evaluate the strengths and weaknesses of these languages. In order to gain a better understanding of features in and their use by programmers of these languages, we perform a study which compares the two languages GOAL and POSH. The study aims at advancing our knowledge of the benefits of using agentoriented languages and at contributing to the evolution of these languages. The main focus of the study is on the usability of both languages and the differences between novice and more advanced programmers that use either language. As POSH requires Java programming experience, we expected novice POSH programmers to perform better on the tasks than novice GOAL programmers whereas we hypothesized this difference would not be observed between more advanced programmers. However, results suggest that there is no significant difference. The study does suggest that general experience and tooling support can make a difference. Analysis of the tasks and the observations made about the use of the languages, moreover, suggests ways to improve the experimental design in such a way that differences in usability of the frameworks could be established.
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Paper Nr:	131
Title:	A Structural Subsumption based Similarity Measure for the Description Logic ALEH
Authors:	Boontawee Suntisrivaraporn and Suwan Tongphu
Abstract:	Description Logics (DLs) are a family of logic-based knowledge representation formalisms, which can be used to develop ontologies in a formally well-founded way. The standard reasoning service of subsumption has proved indispensable in ontology design and maintenance. This checks, relative to the logical definitions in the ontology, whether one concept is more general/specific than another. When no subsumption relationship is identified, however, no information about the two concepts can be given. This work extends from an existing work on similarity measure in ELH to the more expressive description logic ALEH . We introduce generalizations of the notion of normalization and homomorphism in ALEH which are then employed at the heart of our semantic similarity measure. The proposed similarity measure computes a numerical degree of similarity between two ALEH concept descriptions despite not being in the subsumption relation.
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Paper Nr:	152
Title:	Shifts of Attention During Spatial Language Comprehension - A Computational Investigation
Authors:	Thomas Kluth, Michele Burigo and Pia Knoeferle
Abstract:	Regier and Carlson (2001) have investigated the processing of spatial prepositions and developed a cognitive model that formalizes how spatial prepositions are evaluated against depicted spatial relations between objects. In their Attentional Vector Sum (AVS) model, a population of vectors is weighted with visual attention, rooted at the reference object and pointing to the located object. The deviation of the vector sum from a reference direction is then used to evaluate the goodness-of-fit of the spatial preposition. Crucially, the AVS model assumes a shift of attention from the reference object to the located object. The direction of this shift has been challenged by recent psycholinguistic and neuroscientific findings. We propose a modified version of the AVS model (the rAVS model) that integrates these findings. In the rAVS model, attention shifts from the located object to the reference object in contrast to the attentional shift from the reference object to the located object implemented in the AVS model. Our model simulations show that the rAVS model accounts for both the data that inspired the AVS model and the most recent findings.
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Paper Nr:	153
Title:	Tracking The Invisible Man - Hidden-object Detection for Complex Visual Scene Understanding
Authors:	Joanna Isabelle Olszewska
Abstract:	Reliable detection of objects of interest in complex visual scenes is of prime importance for video-surveillance applications. While most vision approaches deal with tracking visible or partially visible objects in single or multiple video streams, we propose a new approach to automatically detect all objects of interest being part of an analyzed scene, even those entirely hidden in a camera view whereas being present in the scene. For that, we have developed an innovative artificial-intelligence framework embedding a computer vision process fully integrating symbolic knowledge-based reasoning. Our system has been evaluated on standard datasets consisting of video streams with real-world objects evolving in cluttered, outdoor environment under difficult lighting conditions. Our proposed approach shows excellent performance both in detection accuracy and robustness, and outperforms state-of-the-art methods.
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Short Papers

Paper Nr:	4
Title:	Intuitionistic De Morgan Verification and Falsification Logics
Authors:	Norihiro Kamide
Abstract:	In this paper, two new logics called intuitionistic De Morgan verification logic DV and intuitionistic De Morgan falsification logic DF are introduced as a Gentzen-type sequent calculus. The logics DV and DF have De Morgan-like laws with respect to implication and co-implication. These laws are analogous to the well-known De Morgan laws with respect to conjunction and disjunction. On the one hand, DV can appropriately represent verification (or justification) of incomplete information, on the other hand DF can appropriately represent falsification (or refutation) of incomplete information. Some theorems for embedding DV into DF and vice versa are shown. The cut-elimination theorems for DV and DF are proved, and DV and DF are also shown to be paraconsistent and decidable.
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Paper Nr:	7
Title:	Negative Norms Detection Technique in Open Normative Multi-agent Systems
Authors:	Muhsen Hammoud, Alicia Y. C. Tang and Azhana Ahmad
Abstract:	Social norms main objective is to regulate autonomous agents’ behaviour in an open normative multi-agent system. Norms in these societies are dynamically created and disappeared according to the society’s needs. Consequently, norms effects on agents or on the environment are not observable at the moment of creation. Norms practicing consequences might be either positive, like increasing the educational level of a society by conducting social discussions. Or negative, like causing money loss in gambling. Or the norm might have neutral consequences. In this paper, we propose a technique to detect negative norms in an open normative multi-agent system. Our technique has two main stages: i) Observation and ii) Analysis. The observation stage relies on the overhearing approach of monitoring where the messages that are exchanged between agents are observable. All observations are then analysed in order to detect negative norms. Negativity of a norm is based on its effect on agents or on the environment. In this technique, we adopted ATN concept to represent norms. This technique is implemented using Java and JADE. Testing results of this technique shows that it works properly, and detects negative norms according to the defined negativity threshold.
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Paper Nr:	8
Title:	Anomaly Detection using B-spline Control Points as Feature Space in Annotated Trajectory Data from the Maritime Domain
Authors:	Mathias Anneken, Yvonne Fischer and Jürgen Beyerer
Abstract:	The detection of anomalies and outliers is an important task for surveillance applications as it supports operators in their decision making process. One major challenge for the operators is to keep focus and not to be overwhelmed by the amount of information supplied by different sensor systems. Therefore, helping an operator to identify important details in the incoming data stream is one possibility to strengthen their situation awareness. In order to achieve this aim, the operator needs a detection system with high accuracy and low false alarm rates, because only then the system can be trusted. Thus, a fast and reliable detection system based on b-spline representation is introduced. Each trajectory is estimated by its cubic b-spline representation. The normal behavior is then learned by different machine learning algorithm like support vector machines and artificial neural networks, and evaluated by using an annotated real dataset from the maritime domain. The results are compared to other algorithms.
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Paper Nr:	24
Title:	A Connexionist Model for Emotions in Digital Agents
Authors:	Jean-Claude Heudin
Abstract:	This paper introduces a bio-inspired model of affects for digital agents. This model provides three distinct layers: emotions as short-term affect, moods as medium-term affect, and personality as a long-term affect. It describes an implementation based on a connexionist architecture using a dedicated neural network designed for the “Living Mona Lisa” research project.
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Paper Nr:	28
Title:	Exposing Design Mistakes During Requirements Engineering by Solving Constraint Satisfaction Problems to Obtain Minimum Correction Subsets
Authors:	Alexander Diedrich, Björn Böttcher and Oliver Niggemann
Abstract:	In recent years, the complexity of production plants and therefore of the underlying automation systems has grown significantly. This makes the manual design of automation systems increasingly difficult. As a result, errors are found only during production, plant modifications are hindered by not maintainable automation solutions and criteria such as energy efficiency or cost are often not optimized. This work shows how utilizing Minimum Correction Subsets (MCS) of a Constraint Satisfaction Problem improves the collaboration of automation system designers and prevents inconsistent requirements and thus subsequent errors in the design. This opens up a new field of application for constraint satisfaction techniques. As a use case, an example from the field of automation system design is presented. To meet the automation industry’s requirement for standardised solutions that assure reliability, the calculation of MCS is formulated in such a way that most constraint solvers can be used without any extensions. Experimental results with typical problems demonstrate the practicalness concerning runtime and hardware resources.
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Paper Nr:	33
Title:	Duality in Some Intuitionistic Paraconsistent Logics
Authors:	Norihiro Kamide
Abstract:	Duality in constructive (or intuitionistic) logics is an important basic property since the dual counterpart of a given constructive logic can obtain a refutation or falsification of the information or knowledge which is described by the given logic. In this paper, duality in some intuitinistic paraconsistent logics is investigated. A constructive connexive logic (connexive logic for short) and Nelson’s paraconsistent four-valued logic are addressed as an example of such intuitionistic paraconsistent logics. A new logic called dual connexive logic (dCN), which is the dual counterpart of the connexive logic (CN), is introduced as a Gentzen-type sequent calculus. Some theorems for embedding dCN into CN and vice versa, which represent the duality between them, are shown. Similar embedding results cannot be shown for Nelson’s paraconsistent four-valued logic. But, similar embedding results can be shown for an extended Nelson logic with co-implication.
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Paper Nr:	34
Title:	Simulating Complex Systems - Complex System Theories, Their Behavioural Characteristics and Their Simulation
Authors:	Rabia Aziza, Amel Borgi, Hayfa Zgaya and Benjamin Guinhouya
Abstract:	Complexity science offers many theories such as chaos theory and coevolutionary theory. These theories illustrate a large set of real life systems and help decipher their nonlinear and unpredictable behaviours. Categorizing an observed Complex System among these theories depends on the aspect that we intend to study, and it can help better understand the phenomena that occur within the system. This article aims to give an overview on Complex Systems and their modelling. Therefore, we compare these theories based on their main behavioural characteristics, e.g. emergence, adaptability, and dynamism. Then we compare the methods used in the literature to model and simulate Complex Systems, and we propose and discuss simple guidelines to help understand one’s Complex System and choose the most adequate model to simulate it.
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Paper Nr:	43
Title:	Gait-based Recognition for Human Identification using Fuzzy Local Binary Patterns
Authors:	Amer G. Binsaadoon and El-Sayed M. El-Alfy
Abstract:	With the increasing security breaches nowadays, automated gait recognition has recently received increasing importance in video surveillance technology. In this paper, we propose a method for human identification at distance based on Fuzzy Local Binary Pattern (FLBP). After the Gait Energy Image (GEI) is generated as a spatiotemporal summary of a gait video sequence, a multi-region partitioning is applied and FLBP based features are extracted for each region. We also evaluate the performance under the variation of some factors including viewing angle, clothing and carrying conditions. The experimental work showed that GEI-FLBP with partitioning has remarkably enhanced the identification accuracy.
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Paper Nr:	56
Title:	Risk-aware Planning in BDI Agents
Authors:	Ronan Killough, Kim Bauters, Kevin McAreavey, Weiru Liu and Jun Hong
Abstract:	The ability of an autonomous agent to select rational actions is vital in enabling it to achieve its goals. To do so effectively in a high-stakes setting, the agent must be capable of considering the risk and potential reward of both immediate and future actions. In this paper we provide a novel method for calculating risk alongside utility in online planning algorithms. We integrate such a risk-aware planner with a BDI agent, allowing us to build agents that can set their risk aversion levels dynamically based on their changing beliefs about the environment. To guide the design of a risk-aware agent we propose a number of principles which such an agent should adhere to and show how our proposed framework satisfies these principles. Finally, we evaluate our approach and demonstrate that a dynamically risk-averse agent is capable of achieving a higher success rate than an agent that ignores risk, while obtaining a higher utility than an agent with a static risk attitude.
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Paper Nr:	63
Title:	Towards an Agent-driven Software Architecture Aligned with User Stories
Authors:	Yves Wautelet, Samedi Heng, Manuel Kolp and Christelle Scharff
Abstract:	Agile principles have taken an increasing importance in the last decades. Software Architecture (SA) definition is perceived as a non-agile practice as it is executed in a top-down manner, reminding waterfall development, and sometimes imposes heavy documentation. This paper proposes to systematically build an agent-oriented SA from a set of User Stories (US), the core artifact to document requirements in agile methodologies. Previous research has allowed to define a unified US meta-model for the generation of templates relating WHO, WHAT and WHY elements. This meta-model’s elements define a syntax issued from practitioners templates associated with semantics from Goal Oriented Requirements Engineering frameworks, more precisely i*. With a set of US following the templates of this previous model, the link between the US and SA concepts is systematically studied and a transformation process is proposed. The SA can decline agent behaviors aligned with requirements and organizational behaviors. Moreover, requirements (thus US) are subject to evolution through agile iterations; the SA can evolve with these changes in a semi-automatic manner. We thus argue that the Agent-SA produced with our transformation process contributes to the overall project agility.
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Paper Nr:	64
Title:	Parallel Implementation of Spatial Pooler in Hierarchical Temporal Memory
Authors:	Marcin Pietron, Maciej Wielgosz and Kazimierz Wiatr
Abstract:	Hierarchical Temporal Memory is a structure that models some of the structural and algorithmic properties of the neocortex. HTM is a biological model based on the memory-prediction theory of brain. HTM is a method for discovering and learning of observed input patterns and sequences, building an increasingly complex models. HTM combines and extends approaches used in sparse distributed memory, bayesian networks, spatial and temporal clustering algorithms, using a tree-shaped hierarchy neural networks. It is quite a new model of deep learning process, which is very efficient technique in artificial intelligence algorithms. HTM like other deep learning models (Boltzmann machine, deep belief networks etc.) has structure which can be efficiently processed by parallel machines. Modern multi-core processors with wide vector processing units (SSE, AVX), GPGPU are platforms that can tremendously speed up learning, classifying or clustering algorithms based on deep learning models (e.g. Cuda Toolkit 7.0). The current bottleneck of this new flexible artifficial intelligence model is efficiency. This article focuses on parallel processing of HTM learning algorithms in parallel hardware platforms. This work is the first one about implementation of HTM architecture and its algorithms in hardware accelerators. The article doesn’t study quality of the algorithm.
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Paper Nr:	75
Title:	Authorship Attribution using Variable Length Part-of-Speech Patterns
Authors:	Yao Jean Marc Pokou, Philippe Fournier-Viger and Chadia Moghrabi
Abstract:	Identifying the author of a book or document is an interesting research topic having numerous real-life applications. A number of algorithms have been proposed for the automatic authorship attribution of texts. However, it remains an important challenge to find distinct and quantifiable features for accurately identifying or narrowing the range of likely authors of a text. In this paper we propose a novel approach for authorship attribution, which relies on the discovery of variable-length sequential patterns of parts of speech to build signatures representing each author’s writing style. An experimental evaluation using 10 authors and 30 books, consisting of 2,615,856 words, from Project Gutenberg was carried. Results show that the proposed approach can accurately classify texts most of the time using a very small number of variable-length patterns. The proposed approach is also shown to perform better using variable-length patterns than with fixed-length patterns (bigrams or trigrams).
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Paper Nr:	78
Title:	Ensemble UCT Needs High Exploitation
Authors:	S. Ali Mirsoleimani, Aske Plaat, Jaap van den Herik and Jos Vermaseren
Abstract:	Recent results have shown that the MCTS algorithm (a new, adaptive, randomized optimization algorithm) is effective in a remarkably diverse set of applications in Artificial Intelligence, Operations Research, and High Energy Physics. MCTS can find good solutions without domain dependent heuristics, using the UCT formula to balance exploitation and exploration. It has been suggested that the optimum in the exploitation-exploration balance differs for different search tree sizes: small search trees needs more exploitation; large search trees need more exploration. Small search trees occur in variations of MCTS, such as parallel and ensemble approaches. This paper investigates the possibility of improving the performance of Ensemble UCT by increasing the level of exploitation. As the search trees become smaller we achieve an improved performance. The results are important for improving the performance of large scale parallelism of MCTS.
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Paper Nr:	85
Title:	Artificial Student Agents and Course Mastery Tracking
Authors:	Linda DuHadway and Thomas C. Henderson
Abstract:	In an effort to meet the changing landscape of education many departments and universities are offering more online courses – a move that is likely to impact every department in some way (Rover et al., 2013). This will require more instructors create online courses, and we describe here how agents and dynamic Bayesian networks can be used to inform this process. Other innovations in instructional strategies are also widely impacting educators (Cutler et al., 2012) including peer instruction, flipped classrooms, problem-based learning, just-in-time teaching, and a variety of active learning strategies. Implementing any of these strategies requires changes to existing courses. We propose ENABLE, a graph-based methodology, to transform a standard linear in-class delivery approach to an on-line, active course delivery system (DuHadway and Henderson, 2015). The overall objectives are: (1) to create a set of methods to analyze the content and structure of existing learning materials that have been used in a synchronous, linearly structured course and provide insight into the nature and relations of the course material and provide alternative ways to organize them, (2) to provide a Bayesian framework to assist in the discovery of causal relations between course learning items and student performance, and (3) to develop some simple artificial student agents and corresponding behavior models to probe the methods’ efficacy and accuracy. In this paper, we focus on our efforts on the third point.
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Paper Nr:	89
Title:	Acyclic Recursion with Polymorphic Types and Underpecification
Authors:	Roussanka Loukanova
Abstract:	The paper extends Moschovakis higher-order type theory of acyclic recursion by adding type polymorphism. We extend the type system of the theory to model parametric information that pertains to underspecified types. Different kinds of type polymorphism are presented via type variables and recursion constructs for alternative, disjunctive type assignments. Based on the new type system, we extend the reduction calculus of the theory of acyclic recursion. We motivate the type polymorphism with examples from English language.
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Paper Nr:	104
Title:	Learning Models of Human Behaviour from Textual Instructions
Authors:	Kristina Yordanova and Thomas Kirste
Abstract:	There are various activity recognition approaches that rely on manual definition of precondition-effect rules to describe human behaviour. These rules are later used to generate computational models of human behaviour that are able to reason about the user behaviour based on sensor observations. One problem with these approaches is that the manual rule definition is time consuming and error prone process. To address this problem, in this paper we propose an approach that learns the rules from textual instructions. In difference to existing approaches, it is able to learn the causal relations between the actions without initial training phase. Furthermore, it learns the domain ontology that is used for the model generalisation and specialisation. To evaluate the approach, a model describing cooking task was learned and later applied for explaining seven plans of actual human behaviour. It was then compared to a hand-crafted model describing the same problem. The results showed that the learned model was able to recognise the plans with higher overall probability compared to the hand-crafted model. It also learned a more complex domain ontology and was more general than the hand-crafted model. In general, the results showed that it is possible to learn models of human behaviour from textual instructions which are able to explain actual human behaviour.
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Paper Nr:	107
Title:	Reconstruction of Everyday Life Behaviour based on Noisy Sensor Data
Authors:	Max Schröder, Sebastian Bader, Frank Krüger and Thomas Kirste
Abstract:	The reconstruction of human activities is an important prerequisite to provide assistance. In this paper, we present an activity and plan recognition approach which is based on causal models of human activities. We show, that it is possible to estimate current activities, the underlying goal of the user, and context information about the state of the environment from noisy sensor data. Therefore we use real world data obtained from a smart home system while observing unrestricted activities of daily living in an inhabited flat. We evaluate the accuracy of the recognition for simulated data of different granularity and data obtained from the smart home system. We furthermore show that performance measures solely based on action sequences are not sufficient to evaluate a recognition system.
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Paper Nr:	108
Title:	The AIS Project: Boosting Information Extraction from Legal Documents by using Ontologies
Authors:	María G. Buey, Angel Luis Garrido, Carlos Bobed and Sergio Ilarri
Abstract:	In the legal field, it is a fact that a large number of documents are processed every day by management companies with the purpose of extracting data that they consider most relevant in order to be stored in their own databases. Despite technological advances, in many organizations, the task of examining these usually-extensive documents for extracting just a few essential data is still performed manually by people, which is expensive, time-consuming, and subject to human errors. Moreover, legal documents usually follow several conventions in both structure and use of language, which, while not completely formal, can be exploited to boost information extraction. In this work, we present an approach to obtain relevant information out from these legal documents based on the use of ontologies to capture and take advantage of such structure and language conventions. We have implemented our approach in a framework that allows to address different types of documents with minimal effort. Within this framework, we have also regarded one frequent problem that is found in this kind of documentation: the presence of overlapping elements, such as stamps or signatures, which greatly hinders the extraction work over scanned documents. Experimental results show promising results, showing the feasibility of our approach.
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Paper Nr:	109
Title:	Speech Emotion Recognition with Log-Gabor Filters
Authors:	Yu Gu, Eric Postma, Hai Xiang Lin and Jaap van den Herik
Abstract:	Speech emotion recognition has been a prevalent research topic in recent years. Existing speech emotion recognition approaches mainly involve processing and analyzing speech signals, in order to discern the speaker’s emotions in speech. 2D Gabor filters have been used to extract the spectro-temporal features of the emotional information from spectrogram. We used Gabor filters to find and extract the major feature patterns for different emotions in spectrogram in our previous study which had concentrated on the parts of a sentence that demonstrated intensive expressions of emotions. In this paper, however, we further categorize emotional expressions in a sentence into primary and secondary ones according to its intensiveness and reveal the feature patterns of the secondary emotional expressions in spectrogram. We conducted feature extraction using Gabor filters on the feature patterns of both primary and secondary emotional expressions. Our experimental results outperformed those from the state-of-the-art and primary-patterns-focused algorithms. This demonstrates that secondary emotional feature patterns can be extracted and used to further improve the accuracy of speech emotion recognition.

Paper Nr:	110
Title:	Gait Recognition using Dynamic Conditional Random Fields
Authors:	Mabrouka Hagui and Mohamed Ali Mahjoub
Abstract:	Gait is a recent biometric technology which aims at identifying people at distance by the way they walk. It has the advantage of recognizing person without their cooperation and doesn’t need high resolution of image. In this paper, we present a new discriminative method for gait recognition using dynamic conditional random fields (CRF). We use a dynamic CRF model to combine two classifiers a spatial classifier which assigns a label to a local features (SURF descriptors) and temporal classifier which uses a motion History Image (MHI). The proposed framework, firstly extracts the human silhouette. Secondly, it takes out spatial and temporal cues from each frame; the proposed system combines spatial and temporal features to improve the performance of human identification. In this paper, we compare two different dynamic CRFs models for analyzing human walk, the two models are Factorial CRFs and Hidden dynamic CRFs.

Paper Nr:	112
Title:	Toward a Guide Agent who Actively Intervene Inter-user Conversation – Timing Definition and Trial of Automatic Detection using Low-level Nonverbal Features
Authors:	Hung-Hsuan Huang, Shochi Otogi, Ryo Hotta and Kyoji Kawagoe
Abstract:	As the advance of embodied conversational agent (ECA) technologies, there are more and more real-world deployed applications of ECA’s. The guides in museums or exhibitions are typical examples. However, in these situations, the agent systems usually need to engage groups of visitors rather than individual ones. In such a multi-user situation, which is much more complex than single user one, specialized additional features are required. One of them is the ability for the agent to smoothly intervene user-user conversation. In order to realize this, at first, a Wizard-of-Oz (WOZ) experiment was conducted for collecting human interaction data. By analyzing the collected data corpus, four kinds of timings that potentially allow the agent to do intervention were found. The collected corpus was then annotated with these defined timings by recruited evaluators with a dedicated and intuitive tool. Finally, as the trial of the possibility of automatic detection on these timings, the use of non-verbal low level features were able to achieve a moderate accuracy.
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Paper Nr:	113
Title:	Skyline Computation on Commercial Data
Authors:	Michael Galli, Stefan Schnürle, Ruedi Arnold and Marc Pouly
Abstract:	Many different skyline algorithms for preference-based search have been proposed and compared in the literature, but most of these evaluations were based on synthetic data. In this paper, we present a case study of skyline computation on commercial data that we consider representative for many e-commerce platforms. The results of our measurements differ significantly from the results reported on synthetic data.
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Paper Nr:	121
Title:	simπ: A Concept Similarity Measure under an Agent’s Preferences in Description Logic ELH
Authors:	Teeradaj Racharak, Boontawee Suntisrivaraporn and Satoshi Tojo
Abstract:	In Description Logics (DLs), concept similarity measures (CSMs) aim at identifying a degree of commonality between two given concepts and are often regarded as a generalization of the classical reasoning problem of equivalence. That is, any two concepts are equivalent if their similarity degree is one, and vice versa. When two concepts are not equivalent, the level of similarity varies depending not only on the objective factors (i.e. the concept descriptions) but also on the subjective factors (i.e. the agent’s preferences). This work presents the notion of a general preference profile to be used in existing similarity measures and exemplifies its applicability with the similarity measure for the DL ELH , called simπ . We show that our measure is expressible for all aspects of preference profile and prove that simπ is preference-invariant w.r.t. equivalence, i.e. similarity between two equivalent concepts is always one regardless of agents’ preferences.
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Paper Nr:	122
Title:	Human Activity Recognition - Using Sensor Data of Smartphones and Smartwatches
Authors:	Bishoy Sefen, Sebastian Baumbach, Andreas Dengel and Slim Abdennadher
Abstract:	Unobtrusive and mobile activity monitoring using ubiquitous, cheap and widely available technology is the key requirement for human activity recognition supporting novel applications, such as health monitoring. With the recent progress in wearable technology, pervasive sensing and computing has become feasible. However, recognizing complex activities on light-weight devices is a challenging task. In this work, a platform to combine off-the-shelf sensors of smartphones and smartwatches for recognizing human activities in real-time is proposed. In order to achieve the best tradeoff between the system’s computational complexity and recognition accuracy, several evaluations were carried out to determine which classification algorithm and features to be used. Therefore, a data set from 16 participants was collected that includes normal daily activities and several fitness exercises. The analysis results showed that naive Bayes performs best in our experiment in both the accuracy and efficiency of classification, while the overall classification accuracy is 87% ± 2.4.
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Paper Nr:	124
Title:	Towards a Real-time Game Description Language
Authors:	Jakub Kowalski and Andrzej Kisielewicz
Abstract:	For the sake of the General Game Playing competition, the Game Description Language (GDL) has been developed as a high-level knowledge representation formalism, able to describe any finite, n-player, turnbased, deterministic, full-information game. The last two restrictions were removed by the later extension called GDL-II. In this paper, we discuss our extension of GDL, called rtGDL, that makes it possible to describe a large variety of games involving a real-time factor. We consider its effectiveness and expressiveness, arguing that this is a promising direction of research in the field of General Game Playing.
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Paper Nr:	129
Title:	Actuation-based Shape Representation Applied to Engineering Document Analysis
Authors:	Thomas C. Henderson, Narong Boonsiribunsum and Anshul Joshi
Abstract:	We propose that human generated drawings (including text and graphics) can be represented in terms of actuation processes required to produce them in addition to the visual or geometric properties. The basic theoretical tool is the wreath product introduced by Leyton (Leyton, 2001) (a special form of the semi-direct product from group theory which expresses the action of a control group on a fiber group) which can be used to describe the basic strokes used to form characters and other elements of the drawing. This captures both the geometry (points in the plane) of a shape as well as a generative model (actuation sequences on a kinematic structure). We show that this representation offers several advantages with respect to robust and effective semantic analysis of CAD drawings in terms of classification rates. Document analysis methods have been studied for several decades and much progress has been made; see (Henderson, 2014) for an overview. However, there are many classes of document images which still pose serious problems for effective semantic analysis. Of particular interest here are CAD drawings, and more specifically sets of scanned drawings for which either the electronic CAD no longer exists, or which were produced by hand. We demonstrate results on a set of CAD-generated drawings for automotive parts.
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Paper Nr:	138
Title:	Collaborative Explanation and Response in Assisted Living Environments Enhanced with Humanoid Robots
Authors:	Antonis Bikakis, Patrice Caire, Keith Clark, Gary Cornelius, Jiefei Ma, Rob Miller, Alessandra Russo and Holger Voos
Abstract:	An ageing population with increased social care needs has provided recent impetus for research into assisted living technologies, as the need for different approaches to providing supportive environments for senior citizens becomes paramount. Ambient intelligence (AmI) systems are already contributing to this endeavour. A key feature of future AmI systems will be the ability to identify causes and explanations for changes to the environment, in order to react appropriately. We identify some of the challenges that arise in this respect, and argue that an iterative and distributed approach to explanation generation is required, interleaved with directed data gathering. We further argue that this can be realised by developing and combining state-of-the art techniques in automated distributed reasoning, activity recognition, robotics, and knowledge-based control.
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Paper Nr:	140
Title:	Detecting Intelligence - The Turing Test and Other Design Detection Methodologies
Authors:	George D. Montañez
Abstract:	“Can machines think?” When faced with this “meaningless” question, Alan Turing suggested we ask a different, more precise question: can a machine reliably fool a human interviewer into believing the machine is human? To answer this question, Turing outlined what came to be known as the Turing Test for artificial intelligence, namely, an imitation game where machines and humans interacted from remote locations and human judges had to distinguish between the human and machine participants. According to the test, machines that consistently fool human judges are to be viewed as intelligent. While popular culture champions the Turing Test as a scientific procedure for detecting artificial intelligence, doing so raises significant issues. First, a simple argument establishes the equivalence of the Turing Test to intelligent design methodology in several fundamental respects. Constructed with similar goals, shared assumptions and identical observational models, both projects attempt to detect intelligent agents through the examination of generated artifacts of uncertain origin. Second, if the Turing Test rests on scientifically defensible assumptions then design inferences become possible and cannot, in general, be wholly unscientific. Third, if passing the Turing Test reliably indicates intelligence, this implies the likely existence of a designing intelligence in nature.
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Paper Nr:	141
Title:	Optimising Flexibility for Simple Temporal Networks
Authors:	Cees Witteveen
Abstract:	We generalise a recently proposed concurrent flexibility metric to overcome some of its shortcomings. We show that these shortcomings can be removed if one selects an optimal subset of variables for which the concurrent flexibility is determined. The flexibility of the remaining variables does not play a role in the determination of the flexibility of the system. We present a preliminary experimental evaluation of the improvement in concurrent flexibility that can be obtained by comparing some (approximation) algorithms. Their performance on several benchmark sets is evaluated. As a result, in some cases the concurrent flexibility of an STN can be enhanced by 20 - 50%.
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Paper Nr:	142
Title:	Knowledge Base Compilation for Inconsistency Measures
Authors:	Said Jabbour, Badran Raddaoui and Lakhdar Sais
Abstract:	Measuring conflicts is recognized as an important issue for handling inconsistencies. Indeed, an inconsistency measure can be employed to support the knowledge engineer in building a consistent knowledge base or repairing an inconsistent one. Good measures are supposed to satisfy a set of rational properties. However, defining sound properties is sometimes problematic. In (Jabbour et al., 2014c), the authors proposed a new prime implicates based approach to identify the variables involved in the contradiction, and a refinement of the notion of minimal inconsistent subsets (MUSes) in propositional knowledge bases. In this article, we establish a bridge between the conflicting variables in knowledge bases and the three valued semantics by compiling each formula of the base into its prime implicates. We then extend hitting sets for MUSes to hitting sets of the set of deduced MUSes (DMUSes) based on prime implicates representation. This leads to an interesting family of inconsistency metrics.
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Paper Nr:	144
Title:	On Evaluation of Natural Language Processing Tasks - Is Gold Standard Evaluation Methodology a Good Solution?
Authors:	Vojtěch Kovář, Miloš Jakubíček and Aleš Horák
Abstract:	The paper discusses problems in state of the art evaluation methods used in natural language processing (NLP). Usually, some form of gold standard data is used for evaluation of various NLP tasks, ranging from morphological annotation to semantic analysis. We discuss problems and validity of this type of evaluation, for various tasks, and illustrate the problems on examples. Then we propose using application-driven evaluations, wherever it is possible. Although it is more expensive, more complicated and not so precise, it is the only way to find out if a particular tool is useful at all.
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Paper Nr:	146
Title:	Information Hiding: Ethics and Safeguards for Beneficial Intelligence
Authors:	Aaron Hunter
Abstract:	Communication involves transferring information from one agent to another. An intelligent agent, either human or machine, is often able to choose to hide information in order to protect their own interests. In this paper, we examine the significance of information hiding from the perspective of beneficial intelligence. Is a computational agent ever justified in preventing human users from accessing information? Conversely, are humans ever under any form of obligation to share information with a computional agent? We discuss the situation from an ethical perspective, and we also address a more pragmatic question: How can we develop safeguards to ensure that machines do not keep secrets in a malicious manner? We suggest that a viable solution to this problem already exists.
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Paper Nr:	147
Title:	The Benefit of Control Knowledge and Heuristics During Search in Planning
Authors:	Jindřich Vodrážka and Roman Barták
Abstract:	The overall performance of classical planner depends heavily on the domain model which can be enhanced by adding control knowledge and heuristics. Both of them are known techniques which can boost the search process in exchange for some computational overhead needed for their repeated evaluation. Our experiments show that the gain from usage of heuristics and control knowledge is evolving throughout the search process and also depends on the type of search algorithm. We demonstrate the idea using the branch-and-bound and iterative deepening search techniques, both implemented in the Picat planning module.
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Paper Nr:	151
Title:	Interest-Point-Based Landmark Computation for Agents’ Spatial Description Coordination
Authors:	J. I. Olszewska
Abstract:	In applications involving multiple conversational agents, each of these agents has its own view of a visual scene, and thus all the agents must establish common visual landmarks in order to coordinate their space understanding and to coherently share generated spatial descriptions of this scene. Whereas natural language processing approaches contribute to define the common ground through dialogues between these agents, we propose in this paper a computer-vision system to determine the object of reference for both agents efficiently and automatically. Our approach consists in processing each agent’s view by computing the related, visual interest points, and then by matching them in order to extract the salient and meaningful landmark. Our approach has been successfully tested on real-world data, and its performance and design allow its use for embedded robotic system communication.
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Paper Nr:	19
Title:	Developing a Formal Model of Argumentation-based Dialogue
Authors:	Mare Koit
Abstract:	We are considering dialogues in natural language where the participants (A and B) are arguing for and against of doing an action D by B. The participants can have similar or opposite communicative goals. If both A and B have the same goal („B will do D“ or, respectively, „B will not do D“) then they are cooperatively looking for arguments that will eliminate possible obstacles before achieving the goal. If the goals are opposite then the participants exchange arguments and counterarguments and one of them has finally to abandon his or her initial communicative goal. A model of dialogue has being developed which includes a model of argument. An analysis of human-human dialogue corpus is carried out in order to give a preliminary evaluation of the introduced model. A limited version of the model is implemented on the computer. Full implementation is planned as a future work.
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Paper Nr:	21
Title:	Decision Making from Confidence Measurement on the Reward Growth using Supervised Learning - A Study Intended for Large-scale Video Games
Authors:	D. Taralla, Z. Qiu, A. Sutera, R. Fonteneau and D. Ernst
Abstract:	Video games have become more and more complex over the past decades. Today, players wander in visuallyand option- rich environments, and each choice they make, at any given time, can have a combinatorial number of consequences. However, modern artificial intelligence is still usually hard-coded, and as the game environments become increasingly complex, this hard-coding becomes exponentially difficult. Recent research works started to let video game autonomous agents learn instead of being taught, which makes them more intelligent. This contribution falls under this very perspective, as it aims to develop a framework for the generic design of autonomous agents for large-scale video games. We consider a class of games for which expert knowledge is available to define a state quality function that gives how close an agent is from its objective. The decision making policy is based on a confidence measurement on the growth of the state quality function, computed by a supervised learning classification model. Additionally, no stratagems aiming to reduce the action space are used. As a proof of concept, we tested this simple approach on the collectible card game Hearthstone and obtained encouraging results.
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Paper Nr:	40
Title:	Discovering Potential Internal Fraud Models in a Stream of Banking Transactions
Authors:	Fabien Vilar, Marc Le Goc, Philippe Bouche and Pierre-Yves Rolland
Abstract:	Internal frauds in the banking industry cause huge costs to banks and this problem is particularly difficult to address since swindlers are very fanciful and thus constantly make fraud schemata evolve. Fraud detection systems are therefore difficult to design because they must learn continuously from the new fraud models. As a consequence, the detection and modeling of the internal fraud are mainly made by hand, entailing late detection and long delays for the modeling phase. This paper proposes a new theoretical and practical approach for designing fraud detection systems able to detect potentially fraudulent transactions and discover the corresponding fraud pattern used by the swindlers. This approach is based on a particular method of abstraction that reduces the complexity of the problem from O(n2) to O(n), n being transactions number, making it possible to implement it on a mere professional personal computer as a Java program that works in real time and on-line. The paper describes this approach and its results on real-world internal fraud detection, analyzing a transaction database provided by a worldwide French bank.

Paper Nr:	60
Title:	Genetic Algorithm for Weight Optimization in Descriptor based Face Recognition Methods
Authors:	Ladislav Lenc
Abstract:	This paper presents a novel algorithm for weight optimization in descriptor based face recognition methods. We aim at the local texture features that are currently very popular in the face recognition (FR) field. Common concept in such methods is creating histograms of the operator values in rectangular image regions and concatenating them into one large vector called histogram sequence (HS). Usually the facial regions are given equal weight which does not correspond with the reality. We deal with this issue in this work and propose a novel method that optimizes the weights of the regions. The optimization method is based on a genetic algorithm (GA). We test the method together with the local binary patterns (LBP) and patterns of oriented edge magnitudes (POEM) descriptors. We evaluate our algorithms on two real-world corpora: Unconstrained facial images (UFI) database and FaceScrub database. The evaluation results show that the weighted methods outperform the non-weighted ones. The best achieved scores are 68.93% on the UFI database and 57.81% on the FaceScrub database.
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Paper Nr:	77
Title:	Player Profiling using Hidden Markov Models Supported with the Sliding Window Method
Authors:	Alper Kilic, Mehmet Akif Gunes and Sanem Sariel
Abstract:	In this paper, we present a player profiling system applicable for both human players and bots in video games. The Vindinium artificial intelligence (AI) contest is selected as the test-bed for analyzing the performance of our system. In this game, AI bots compete with each other in a systematically generated environment to achieve the highest score. Our profiling method is based on Hidden Markov Model (HMM) constructed by using consecutive actions of AI bots and improved with the initial training phase and our sliding window approach. The method is evaluated for three different performance criteria: recognition of bots, grouping bots that have similar game styles and tracking changes in the strategy of a single bot through the game. The results indicate that the method is promising with 90,04% binary classification success in average.
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Paper Nr:	79
Title:	Mining Frequent Patterns from Correlated Incomplete Databases
Authors:	Badran Raddaoui and Ahmed Samet
Abstract:	Modern real-world applications are forced to deal with inconsistent, unreliable and imprecise information. In this setting, considerable research efforts have been put into the field of caring for the intrinsic imprecision of the data. Indeed, several frameworks have been introduced to deal with imperfection such as probabilistic, fuzzy, possibilistic and evidential databases. In this paper, we present an alternative framework, called correlated incomplete database, to deal with information suffering with imprecision. In addition, correlated incomplete database is studied from a data mining point of view. Since, frequent itemset mining is one of the most fundamental problems in data mining, we propose an algorithm to extract frequent patterns from correlated incomplete databases. Our experiments demonstrate the effectiveness and scalability of our framework.

Paper Nr:	97
Title:	Using Conspiracy Numbers for Improving Move Selection in Minimax Game-Tree Search
Authors:	Quang Vu, Taichi Ishitobi, Jean-Christophe Terrillon and Hiroyuki Iida
Abstract:	In a two-person perfect-information game, Conspiracy Number Search (CNS) was invented as a possible search algorithm but did not find much success. However, we believe that the conspiracy number, which is the core of CNS, has not been used to its full potential. In this paper, we propose a novel way to utilize the conspiracy number in the minimax framework. Instead of using conspiracy numbers separately, we combine them together. An example way of combining conspiracy numbers with the evaluation value is suggested. Empirical results obtained for the game of Othello show the potential of the proposed method.
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Paper Nr:	98
Title:	Digital Analysis of Beautiful Facial Expressions
Authors:	Wilma Latuny, Eric Postma and Jaap van den Herik
Abstract:	Facial attractiveness has been studied extensively in the past decades. Sexual dimorphism, a measure of the physical male or female facial appearance (masculinity or femininity), has been shown to be a reliable indicator of attractiveness. In order to determine the contribution of facial expressions to female attractiveness in relation to femininity, we performed a digital analysis of the facial expressions of videos of Miss World 2013 contestants. We analyzed three types of expressions in isolation or in combination with femininity: (i) facial expressions (represented by facial expression descriptors), (ii) smiling, and (iii) emotional expressions. The scores awarded by the Miss World judges to the contestant videos served as an independent measure of attractiveness. We performed two analyses. In a correlation analysis the three types of expressions were correlated with the attractiveness judgment scores to determine their relation with attractiveness. In a predictive analysis, we trained random decision forests on the task of predicting the attractiveness judgment scores in a leaving-one-out cross validation procedure. The results of the prediction analysis revealed that facial contribute to facial attractiveness, but only in combination with femininity. In contrast, emotional expressions in combination with femininity do not contribute to attractiveness. These findings indicate that facial expressions contribute to female attractiveness, when considered in combination with sexual dimorphism.

Paper Nr:	105
Title:	Comparing Machine Learning Techniques in a Hyperemia Grading Framework
Authors:	L. S. Brea, N. Barreira, A. Mosquera, H. Pena-Verdeal and E. Yebra-Pimentel
Abstract:	Hyperemia is the occurrence of redness in a certain tissue. When it takes place on the bulbar conjunctiva, it can be an early symptom of different pathologies, hence, the importance of its quick evaluation. Experts grade hyperemia as a value in a continuous scale, according to the severity level. As it is a subjective and time consuming task, its automatisation is a priority for the optometrists. To this end, several image features are computed from a video frame that shows the patient’s eye. Then, these features are transformed to the grading scale by means of machine learning techniques. In previous works, we have analysed the performance of several regression algorithms. However, since the experts only use a finite number of values in each grading scale, in this paper we analyse how classifiers perform the task in comparison to regression methods. The results show that the classification techniques usually achieve a lower training error value, but the regression approaches classify correctly a larger number of samples.
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Paper Nr:	120
Title:	Bees Swarm Optimization Metaheuristic Guided by Decomposition for Solving MAX-SAT
Authors:	Youcef Djenouri, Zineb Habbas and Wassila Aggoune-Mtalaa
Abstract:	Decomposition methods aim to split a problem into a collection a collection of smaller interconnected sub-problems. Several research works have explored decomposition methods for solving large optimization problems. Due to its theroretical properties, Tree decomposition has been especially the subject of numerous successfull studies in the context of exact optimization solvers. More recently, Tree decomposition has been successfully used to guide the Variable Neighbor Search (VNS) local search method. Our present contribution follows this last direction and proposes two approaches called BSOGD1 and BSOGD2 for guiding the Bees Swarm Optimization (BSO) metaheuristic by using a decomposition method. More pragmatically, this paper deals with the MAX-SAT problem and uses the Kmeans algorithm as a decomposition method. Several experimental results conducted on DIMACS benchmarks and some other hard SAT instances lead to promising results in terms of the quality of the solutions. Moreover, these experiments highlight a good stability of the two approaches, more especially, when dealing with hard instances like the Parity8 family from DIMACS. Beyond these first promising results, note that this approach can be easily applied to many other optimization problems such as the Weighted MAX-SAT, the MAX-CSP or the coloring problem and can be used with other decomposition methods as well as other metaheuristics.
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Paper Nr:	139
Title:	Differential Evolution based on Decomposition for Solving Multi-objective Optimization Problems
Authors:	Ning Xiong and Miguel Leon
Abstract:	Optimization problems with multiple objectives are often encountered in many scientific and engineering scenarios. The prior works on multi-objective differential evolution (DE) have mainly focused on non- dominated sorting of solutions to handle different objectives at the same time. This paper suggests a new approach to differential evolution which is based on decomposition of the original problem into a set of scalar optimization subproblems. We design a decomposition-based DE algorithm to optimize these scalar subproblems simultaneously by evolving a population of solutions with proper mutation and selection operators. Since the proposed DE algorithm does not involve pairwise comparison and non-dominated sorting of solutions, it would incur lower computational complexity than the dominance-based DE algorithms.

Paper Nr:	148
Title:	Towards Visual Vocabulary and Ontology-based Image Retrieval System
Authors:	Jalila Filali, Hajer Baazaoui Zghal and Jean Martinet
Abstract:	Several approaches have been introduced in image retrieval field. However, many limitations, such as the semantic gap, still exist. As our motivation is to improve image retrieval accuracy, this paper presents an image retrieval system based on visual vocabulary and ontology. We propose, for every query image, to build visual vocabulary and ontology based on images annotations. Image retrieval process is performed by integrating both visual and semantic features and similarities.
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Area 2 - Agents

Full Papers

Paper Nr:	14
Title:	Analysis of Gender-specific Self-adaptors and Their Effects on Agent’s Impressions
Authors:	Tomoko Koda, Takuto Ishioh, Takafumi Watanabe and Yoshihiko Kubo
Abstract:	This paper reports how agents that performs gender-specific self-adaptors are perceived by Japanese evaluators depending on their gender. Human-human interactions among Japanese undergraduate students were analysed with respect to usage of gender-specific self-adaptors in a pre-experiment. Based on the results, a male and a female agent were animated to show these extracted self-adaptors. Evaluation of the interactions between agents that exhibit self-adaptors typically exhibited by human male and female indicated that there is a dichotomy on the impression on the agent between participants’ gender. Male participants showed more favourable impressions on agents that display feminine self-adaptors than masculine ones performed by the female agent, while female participants showed rigorous impressions toward feminine self-adaptors. Although the obtained results were limited to one culture and narrow age range, these results implies the importance of considering the use of self-adaptors and gender in successful human-agent interactions.
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Paper Nr:	62
Title:	Branch-and-Bound Optimization of a Multiagent System for Flow Production using Model Checking
Authors:	Stefan Edelkamp and Christoph Greulich
Abstract:	In this paper we propose the application of a model checker to evaluate a multiagent system that controls the industrial production of autonomous products. As the flow of material is asynchronous at each station, queuing effects arise as long as buffers provide waiting room. Besides validating the design of the system, the core objective of this work is to find plans that optimize the throughput of the system. Instead of mapping the multiagent system directly to the model checker, we model the production line as a set of communicating processes, with the movement of items modeled as communication channels. Experiments shows that the model checker is able to analyze the movements of autonomous products for the model, subject to the partial ordering of the product parts. It derives valid and optimized plans with several thousands of steps using constraint branch-and-bound.
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Paper Nr:	72
Title:	Social Utilities and Personality Traits for Group Recommendation: A Pilot User Study
Authors:	Silvia Rossi and Francesco Cervone
Abstract:	Recommendations to a group of users can be provided by the aggregation of individual users’ recommendations using social choice functions. Standard aggregation techniques do not consider the possibility of evaluating social interactions, roles, and influences among group’s members, as well as their personalities, which are, indeed, crucial factors in the group’s decision-making process. Instead of defining a specific social choice function to take into account such features, the proposed solution relies on the definition of a utility function, for each agent, that takes into account other group members’ preferences. Such function models the level of a user’s altruistic behavior starting from his/her agreeableness personality trait. Once such utility values are evaluated, the goal is to recommend items that maximize the social welfare. Performance is evaluated with a pilot user study and compared with respect to Least Misery. Results showed that while for small groups LM performs slightly better, in the other cases the two methods are comparable.
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Paper Nr:	76
Title:	Adaptive Two-stage Learning Algorithm for Repeated Games
Authors:	Wataru Fujita, Koichi Moriyama, Ken-ichi Fukui and Masayuki Numao
Abstract:	In our society, people engage in a variety of interactions. To analyze such interactions, we consider these interactions as a game and people as agents equipped with reinforcement learning algorithms. Reinforcement learning algorithms are widely studied with a goal of identifying strategies of gaining large payoffs in games; however, existing algorithms learn slowly because they require a large number of interactions. In this work, we constructed an algorithm that both learns quickly and maximizes payoffs in various repeated games. Our proposed algorithm combines two different algorithms that are used in the early and later stages of our algorithm. We conducted experiments in which our proposed agents played ten kinds of games in self-play and with other agents. Results showed that our proposed algorithm learned more quickly than existing algorithms and gained sufficiently large payoffs in nine games.
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Paper Nr:	87
Title:	Switching Behavioral Strategies for Effective Team Formation by Autonomous Agent Organization
Authors:	Masashi Hayano, Yuki Miyashita and Toshiharu Sugawara
Abstract:	In this work, we propose agents that switch their behavioral strategy between rationality and reciprocity depending on their internal states to achieve efficient team formation. With the recent advances in computer science, mechanics, and electronics, there are an increasing number of applications with services/goals that are achieved by teams of different agents. To efficiently provide these services, the tasks to achieve a service must be allocated to agents that have the required capabilities and the agents must not be overloaded. Conventional distributed allocation methods often lead to conflicts in large and busy environments because high-capability agents are likely to be identified as the best team member by many agents, resulting in inefficiency of the entire system due to concentration of task allocation. Our proposed agents switch their strategies in accordance with their local evaluation to avoid conflicts occurring in busy environments. They also establish an organization in which a number of groups are autonomously generated in a bottom-up manner on the basis of dependability in order to avoid the conflict in advance while ignoring tasks allocated by undependable/unreliable agents. We experimentally evaluate our proposed method and analyze the structure of the organization that the agents established.
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Paper Nr:	96
Title:	Bounds on Manipulation by Merging in Weighted Voting Games
Authors:	Ramoni O. Lasisi
Abstract:	Manipulation by merging in weighted voting games is a voluntary action of would-be strategic agents who come together to form a bloc in anticipation of receiving more payoff over the outcomes of games. Previous works have identified manipulation by merging in weighted voting games as a problem. This is because the increase in payoff or power (depending on the settings under consideration) that may be achieved by strategic agents in a game is at the deficit of other agents who are being denied some utilities that are due to them. Thus, the inability to limit (or understand) the effects of this manipulation may undermine the confidence agents have in decisions made via weighted voting games. If the results are not seen as fair, agents may refuse to abide by decisions made in this manner. We propose two non-trivial bounds to characterize the effects of this menace using the well-known Banzhaf power index. The two bounds are also within constant factors.

Paper Nr:	123
Title:	From Arguments and Reviewers to their Simulation - Reproducing a Case-Study
Authors:	Simone Gabbriellini and Francesco Santini
Abstract:	We propose an exploratory study on arguments in Amazon.com reviews. Firstly, we extract positive (in favour of purchase) and negative (against it) arguments from each review concerning a selected product. We accomplish this information extraction manually, scanning all the related reviews. Secondly, we link extracted arguments to the rating score, to the length, and to the date of reviews, in order to undertand how they are connected. As a result, we show that negative arguments are quite sparse in the beginning of such social review-process, while positive arguments are more equally distributed along the timeline. As a final step, we replicate the behaviour of reviewers as agents, by simulating how they assemble reviews in the form of arguments. In such a way, we show we are able to mirror the measured experiment through a simulation that takes into account both positive and negative arguments.
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Short Papers

Paper Nr:	5
Title:	Experimental Investigation for a Human Relationship Formation Support Agent using Information Presentation During Conversation
Authors:	Michimasa Inaba, Kana Otsuka and Kenichi Takahashi
Abstract:	In this paper, we performed an experimental investigation aimed at developing an agent to support the formation of human relationships by supporting the user’s daily communication “casually”, “anytime” and “anywhere”. First, we collected conversations between men and women meeting for the first time, then analyzed what type of support would be effective for the formation of human relationships. Based on the results of this analysis, we performed experiments supporting communication. In the experiment, we provided not only topics to the user during conversation, but also comprehensive presentation of instructions such as expressions, eye contact and gestures. The results confirmed that this significantly improved human relationships after conversation and showed the validity of this support.
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Paper Nr:	9
Title:	Comparison of Improved Floor Field Model and Other Models
Authors:	Hyunwoo Nam, Suyeong Kwak and Chulmin Jun
Abstract:	This study introduces an improved Floor Field Model (FFM) that models pedestrians using realistic physical characteristics (size, shape, and posture). Through comparison with other well-known models, the areas of improvement are elucidated. The FFM is a leading microscopic pedestrian model that uses cellular automation (CA), but it does not accurately reflect the physical characteristics of pedestrians, such as their size, shape, and posture. Therefore, it is difficult for the existing FFM to simulate certain phenomena, such as collisions and friction between pedestrians. This study proposes an improved FFM that can simulate these phenomena, and experiments were carried out to compare this model with other models, such as the existing FFM, Simulex, and Pathfinder, to confirm the improvements. Through this experiment, it was confirmed that inter-pedestrian phenomena, such as collisions, friction, and jamming, could be realistically simulated.
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Paper Nr:	23
Title:	Multi-robot Systems, Machine-Machine and Human-Machine Interaction, and Their Modelling
Authors:	Ulrico Celentano and Juha Röning
Abstract:	The control of multi-agent systems, including multi-robot systems, requires some level of context and environment awareness as well as interaction among the interworked cognitive entities, whether they are artificial or natural. Proper specification of the cognitive functionalities and of the corresponding interfaces helps in achieving the capability to reach interoperability across different operational domains, and to reuse the system design across different application domains. The model for interworking cognitive entities presented in this article, which includes explicitly interworking capabilities, is applied to two major classes of interaction in multi-robot systems. Being the model inspired by both artificial and natural systems, makes it suitable for both machine-machine and human-machine interaction.
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Paper Nr:	26
Title:	Imitating Gender as a Measure for Artificial Intelligence: - Is It Necessary?
Authors:	Huma Shah and Kevin Warwick
Abstract:	Should intelligent agents and robots possess gender? If so, which gender and why? The authors explore one root of the gender-in-AI question from Turing’s introductory male-female imitation game, which matured to his famous Turing test examining machine thinking and measuring its intelligence against humans. What we find is gender is not clear cut and is a social construct. Nonetheless there are useful applications for gender-cued intelligent agents, for example robots caring for elderly patients in their own home.
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Paper Nr:	29
Title:	Comparison of Surveillance Strategies to Identify Undesirable Behaviour in Multi-Agent Systems
Authors:	Sarah Edenhofer, Christopher Stifter, Sven Tomforde, Jan Kantert, Christian Müller-Schloer and Jörg Hähner
Abstract:	Open, distributed systems face the challenge to maintain an appropriate operation performance even in the presence of bad behaving or malicious agents. A promising mechanism to counter the resulting negative impact of such agents is to establish technical trust. In this paper, we investigate strategies to improve the efficiency of trust mechanisms regarding the isolation of undesired participants by means of reputation and accusation techniques. We demonstrate the potential benefit of the developed techniques within simulations of a Trusted Desktop Computing Grid.
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Paper Nr:	47
Title:	Trusting Different Information Sources in a Weather Scenario: A Platform for Computational Simulation
Authors:	Rino Falcone, Alessandro Sapienza and Cristiano Castelfranchi
Abstract:	Thinking about a scenario with possible risk of flooding and landslides caused by weather conditions, it results really interesting to investigate the way in which citizens take decisions on the basis of different information sources they can access. In this work we start describing a platform we realized in order to study this social phenomenon. Then we present some simulative experiments showing how a population of cognitive agents trusting in a different way their information sources can make decisions more or less suited to the several weather patterns. The complexity of decisions is based on the fact that the agents differently trust the various sources of information, which in turn may be differently trustworthy. In our simulations we analyse some interesting case studies, with particular reference to social agents that need to wait others in order to make decision.
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Paper Nr:	68
Title:	Detecting Colluding Attackers in Distributed Grid Systems
Authors:	Jan Kantert, Melanie Kauder, Sarah Edenhofer, Sven Tomforde and Christian Müller-Schloer
Abstract:	Distributed grid systems offer possible benefits in terms of fast computation of tasks. This is accompanied by potential drawbacks due to their openness, the heterogeneity of participants, and the unpredictability of agent behaviour, since agents have to be considered as black-boxes. The utilisation of technical trust within adaptive collaboration strategies has been shown to counter negative effects caused by these characteristics. A major challenge in this context is the presence of colluding attackers that try to exploit or damage the system in a coordinated fashion. Therefore, this paper presents a novel approach to detect and isolate such colluding attackers. The concept is based on observations of interaction patterns and derives a classification of agent communities. Within the evaluation, we demonstrate the benefit of the approach and highlight the highly reliable classification.
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Paper Nr:	84
Title:	Distinguishing AI from Male/Female Dialogue
Authors:	Huma Shah and Kevin Warwick
Abstract:	Without knowledge of other features, can the sex of a person be determined through text-based communication alone? In the first Turing test experiment enclosing 24 human-duo set-ups embedded among machine-human pairs the interrogators erred 50% of the time in assigning the correct sex to a hidden interlocutor identified as human. In this paper we present five transcripts, in four gender blur occurred: Turing test interrogators misclassified male for female and vice versa. In the fifth, machine-human conversation artificial dialogue was branded as female teen. Did stereotypical views on male and female talk sway the judges to assign one way or another? This research is part of ongoing analysis of over 400 tests involving more than 80 human judges. Can we overcome unconscious bias and improve development of agent language?
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Paper Nr:	90
Title:	Enabling Semantic User Context to Enhance Twitter Location Prediction
Authors:	Ahmed Galal and Abeer El-Korany
Abstract:	Prediction of user interest and behavior is currently an important research area in social network analysis. Most of the current prediction frameworks rely on analyzing user’s published contents and user’s relationships. Recently the dynamic nature of user’s modelling has been introduced in the prediction frameworks. This dynamic nature would be represented by time tagged attributes such as posts or location check-ins. In this paper, we study the relationships between geo-location information published by users at different times. This geo-location information was used to model user’s interest and behavior in order to enhance prediction of user locations. Furthermore, semantic features such as topics of interest and location category were extracted from this information in order to overcome sparsity of data. Several experiments on real twitter dataset showed that the proposed context-based prediction model which applies machine learning techniques outperformed traditional probabilistic location prediction model that only rely on words extracted from tweets associated with specific locations.
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Paper Nr:	103
Title:	SDfR - Service Discovery for Robots
Authors:	Stefan-Gabriel Chitic, Julien Ponge and Olivier Simonin
Abstract:	Multi-robots systems require dedicated tools and models for their design and the deployment. Our approach proposes service-oriented architecture that can simplify the development and deployment. In order to solve the problem of neighbors and service discovery in an ad-hoc network, the fleet robot needs a protocol that is able to constantly discover new robots in its coverage area. To this end we propose a robotic middleware, SDfR, that is able to provide service discovery. This protocol is an extension of the Simple Service Discovery Protocol (SSDP) used in Universal Plug and Play (UPnP) to dynamic networks generated by the mobility of the robots. Even if SDfR is platform independent, we propose a ROS (ROS, 2014) integration in order to facilitate the usage. We evaluate a series of overhead benchmarking across static and dynamic scenarios. We also present some use-cases where our proposal was successfully tested.
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Paper Nr:	119
Title:	Agent-based MapReduce Processing in IoT
Authors:	Ichiro Satoh
Abstract:	This paper presents an agent-based framework for processing data at nodes on the Internet of Things (IoT). The framework is based on MapReduce processing, where the MapReduce processing and its clones are popular but inherently have been designed for high-performance server clusters. It aims at enabling data to be processed at nodes on IoT. The key idea behind it is to deploy programs for data processing at the nodes that contain the target data as a map step by using the duplication and migration of agents and to execute the programs with the local data. It aggregates the results of the programs to certain nodes as a reduce step. We describe the architecture and implementation of the framework, its basic performance, and its application are also described here.
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Paper Nr:	133
Title:	Multi-agent Approach for Return Route Support System Simulation
Authors:	Shouhei Taga, Tomofumi Matsuzawa, Munehiro Takimoto and Yasushi Kambayashi
Abstract:	We propose a system that supports stranded commuters caused by a large-scale disaster. When a large-scale disaster breaks out, buildings may collapse and roads may be damaged and the public transportation systems would be paralyzed. Thus, people working in the city center have to walk back home on foot. The problem is that when those people start walking, the situation along the routes for returning home may be different from that of the pre-disaster. Not only may it be the first time for most of them to walk home, but also the return route may be extremely complex due to many detours. They have to look for alternative routes whenever bridges collapse and fires break out. Making situation become worse, modern people intensively use navigation systems, those systems may be unavailable due to the paralyzed Internet. A large scale disaster may destroy base stations of wireless phones, and even if it does not completely destroy them, extreme congestion may paralyze the communication infrastructure so that not only net-surfing using smartphone, but also collecting information by e-mail may become impossible. To deal with such situations, we are designing a system that provides those unfortunate pedestrians appropriate return routes to their homes without depending on the communication infrastructures. Instead, our proposed system only depends on smartphones of those pedestrians and constructs mobile ad hoc networks (MANET) to collect and disperse useful information. We employ multiple mobile agents extensively for information collection and dispersion. In order to demonstrate the feasibility of our system, we have constructed a preliminary prototype of the simulation system and have conducted numerical experiments.
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Paper Nr:	143
Title:	Is It Reasonable to Employ Agents in Automated Theorem Proving?
Authors:	Max Wisniewski and Christoph Benzmüller
Abstract:	Agent architectures and parallelization are, with a few exceptions, rarely to encounter in traditional automated theorem proving systems. This situation is motivating our ongoing work in the higher-order theorem prover Leo-III . In contrast to its predecessor – the well established prover LEO-II – and most other modern provers, Leo-III is designed from the very beginning for concurrent proof search. The prover features a multiagent blackboard architecture for reasoning agents to cooperate and to parallelize proof construction on the term, clause and search level.
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Paper Nr:	145
Title:	A Trust-based Decision-making Approach Applied to Agents in Collaborative Environments
Authors:	Lucile Callebert, Domitile Lourdeaux and Jean-Paul Barthès
Abstract:	In Virtual Environments for Training, the agents playing the trainee’s teammates must display human-like behaviors. We propose in this paper a preliminary approach to a new trust-based decision-making system that allow agents to reason on collective activities. The agents’ integrity, benevolence and abilities dimensions and their trust beliefs in their teammates’ integrity, benevolence and abilities allow them to reason on the importance the give to their goals and then to select the task that best serves their goals.
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Paper Nr:	12
Title:	Internet of Smart Things - A Study on Embedding Agents and Information in a Device
Authors:	Leo van Moergestel, Melvin van den Berg, Marco Knol, Rick van der Paauw, Kasper van Voorst, Erik Puik, Daniël Telgen and John-Jules Meyer
Abstract:	The term Internet of Things (IoT) is used for situations where one or more devices are connected to a network or possibly the Internet. Most studies focus on the possibilities that arise when a device is capable to share its data with other devices or humans. In this study, the focus is on the device itself and what kind of possibilities an Internet connection gives to the device and its owner or user. Also the data the device needs to participate in a smart way in the IoT are part of this study. Agent technology is the enabling technology for the ideas introduced here. A proof of concept is given, where some concepts proposed in the paper are put into practice.
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Paper Nr:	13
Title:	Requirements Planning with Event Calculus for Self-adaptive Multi-agent System
Authors:	Wei Liu, Feng Yao and Ming Li
Abstract:	Self-adaptation of Multi-agent cooperative systems requires dynamic decision making and planning at runtime. Modeling the contextual and executable requirements of such systems as planning actions and states, this paper proposes a requirements-driven planning approach to self-adaptation. The planning model includes the states of the system context and the actions describing the behaviors of its multiple agents; the interactions between these agents and their environment are computed through an expansion of the requirements-driven planning graph, which is then used to verify whether the agents can collaborate in order to reach the desired goal states from their current states. In addition, the requirements are represented for Event Calculus to facilitate monitoring and reasoning about the actions of agents, achieving requirements driven planning at runtime.
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Paper Nr:	30
Title:	Improved Model of Social Networks Dynamics
Authors:	Jiří Jelínek and Roman Klimeš
Abstract:	Social networks are currently the most studied structures due to their popularity among IT users. In our paper we will focus on the dynamics of the dissemination of information in these networks. We will introduce the advanced heuristic conceptual model of individuals’ behavior in the network which is based on need for information and knowledge for solving specific problems; the proposed multi-agent model of the social networks dynamics is based on this concept. This version of the model was adapted for scale-free and growing networks. Experiments conducted with new model were focused on verifying its behavior with respect to knowledge about the type of modeled networks and on observation of dynamic effects in them; the results will be presented as well.
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Paper Nr:	31
Title:	Thinking With Containers: A Multi-Agent Retrieval Approach for the Case-Based Semantic Search of Architectural Designs
Authors:	Viktor Ayzenshtadt, Christoph Langenhan, Saqib Bukhari, Klaus-Dieter Althoff, Frank Petzold and Andreas Dengel
Abstract:	To provide the retrieval of information, that can be considered useful during the design conceptualization process, with advantages of distributed artificial knowledge, an approach, that distributes retrieval-related and knowledge maintaining tasks among autonomously working and case-based self-learning agents and agent groups, can be used. In this work we present the distributed retrieval system MetisCBR for the architectural design domain, where agents work in groups (containers) on resolving of user queries built with a semantic description model Semantic Fingerprint. The main aim of our approach is to carry out a basis for a considerable retrieval tool for architects, where the combination of case-based reasoning and multi-agent methods helps to achieve valuable and helpful search results in a comprehensive building design collection.
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Paper Nr:	42
Title:	Harnessing Supervised Learning Techniques for the Task Planning of Ambulance Rescue Agents
Authors:	Fadwa Sakr and Slim Abdennadher
Abstract:	One of the challenging problems in Artificial Intelligence and Multi-Agent systems is the RoboCup Rescue project that was established in 2001. The Rescue Simulation provides a broad test bench for many algorithms and approaches in the field of AI. The Simulation presents three types of agents: police agents, firebrigade agents and ambulance agents. Each of them has a crucial role in the rescuing problem. The work presented in this paper focuses on the task planning of the ambulance team whose main role is rescuing the maximum number of civilians. It is obvious that this target is a complicated one due to the number of problems that the agent is faced with. One of the problems is estimating the time each civilian takes to die; the Estimated Time of Death (ETD). Realistic estimations of the ETD will lead to a better performance of the ambulance agents by planning their tasks accordingly. Supervised learning is our approach to learn and predict the ETD civilians leading to an optimized planning of the agents tasks.
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Paper Nr:	50
Title:	A Threatmodel for Trust-based Systems Consisting of Open, Heterogeneous and Distributed Agents
Authors:	Jan Kantert, Lukas Klejnowski, Sarah Edenhofer, Sven Tomforde and Christian Müller-Schloer
Abstract:	Information and communication technology witnesses a raise of open, distributed systems that consist of various heterogeneous elements. Within such an environment, individual elements have to efficiently fulfil their goals, which may require cooperation with others. As a consequence, a variety of threats appears that need to be handled and circumvented in the entity’s behaviour. One major technical approach to provide a working environment for such systems is to introduce technical trust. In this paper, we present a basic threat model that comprises the most important challenges in this context – related to the basic system and the trust management, respectively. In order to illustrate the particular hazardous aspects, we discuss a Desktop Computing Grid application as scenario.
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Paper Nr:	51
Title:	A Mutual Influence-based Learning Algorithm
Authors:	Stefan Rudolph, Sven Tomforde and Jörg Hähner
Abstract:	Robust and optimized agent behavior can be achieved by allowing for learning mechanisms within the underlying adaptive control strategies. Therefore, a classic feedback loop concept is used that chooses the best action for an observed situation – and learns the success by analyzing the achieved performance. This typically reflects only the local scope of an agent and neglects the existence of other agents with impact on the reward calculation. However, there are significant mutual influences among agents population. For instance, the success of a Smart Camera’s control strategy depends (in terms of person detection or 3D-reconstruction) largely on the current strategy performed by its spatially neighbors. In this paper, we compare two concepts to consider such influences within the adaptive control strategy: Distributed W-Learning and Q-Learning in combination with mutual influence detection. We demonstrate that the performance can be improved significantly, if taking detected influences into account.
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Paper Nr:	65
Title:	Post Flash Crash Recovery: An Agent-based Analysis
Authors:	Iryna Veryzhenko and Nathalie Oriol
Abstract:	In this paper we focus on the traders that purely rely on algorithms in their decision making and their impact on market quality during moments of instability. We describe an agent-based framework that successfully reproduces main aspects of flash crash. We simulate the effect of a large liquidity shock generated by a very aggressive market order. We show that, despite the absence of market makers, the electronic order-book architecture favors market resiliency and recovery.
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Paper Nr:	74
Title:	Task Allocation in Multi-robot Systems - A Distributed Computation of a Satisfaction Measurement based Approach
Authors:	Emna Ayari, Sameh Hadouaj and Khaled Ghedira
Abstract:	Task allocation is a key requirement for multi-robot systems functioning in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Different multi-robot task allocation models have been proposed over the past decade. The motivation behind the growing interest in developing robot colonies is to enable team members to work together as a group and to combine their efforts in order to accomplish tasks that cannot be handled by an individual robot. However, the existing approaches still have limited applicability in dynamic scenarios where the value associated with a certain coalition changes very rapidly over time. Moreover, these approaches do not offer an optimal allocation resource method. In fact, the coalition formation can be viewed as a resource allocation problem, where the solution consists in finding the best way to allocate a set of finite available resources to members of a population in order to achieve a given objective. In this paper, we address the problem of coalition formation based on a resource allocation approach. More precisely, we present a new method based on a distributed computation of satisfaction measurement that each robot must compute in order to decide whether it will join the coalition or not. We then give some empirical results to show the effectiveness and scalability of the proposed approach.

Paper Nr:	95
Title:	The Role of Information in Group Formation
Authors:	Stefano Bennati, Leonard Wossnig and Johannes Thiele
Abstract:	A vast body of literature studies problems such as cooperation and coordination in groups, but the reasons why groups exist in the first place and hold together are still not clear: in presence of within-group competition, individuals are better off leaving the group. An environment that is advantageous to groups, e.g. better chances of succeeding at or escaping from predation, seems to play a key role for the existence of groups. Another recurrent explanation in the literature is between-group competition. We argue that information constraints can foster sociable behavior, which in turn is responsible for group creation. We compare, by means of an agent-based simulation, navigation strategies that exploit information about the behavior of others. We find that individuals that have sociable behavior have higher fitness than individualistic individuals for certain environmental configuration.
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Paper Nr:	114
Title:	Ontology-based Access Control Management: Two Use Cases
Authors:	Malik Imran-Daud, David Sanchez and Alexandre Viejo
Abstract:	Access control management is an important area of research within the security field. Several models have been proposed to manage the access rights of users over restricted resources, which are mainly based on defining rules between specific entities and concrete resources. Though these approaches are enough to manage organizations involving a limited number of entities and resources, the specification of rules or constraints for large and heterogeneous scenarios may imply a considerable burden to the administrators. To palliate this problem, we propose a generic ontology-based solution to manage the access control that can greatly simplify and speed up the definition of rules in complex scenarios and that can also improve the interoperability between heterogeneous settings. Moreover, we show its potential by applying it in two highly dynamic and large scenarios, i.e., Online Social Networks (OSNs) and the Cloud.
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Paper Nr:	130
Title:	Crowd Behavior in Alternative@ - Conflicts in the Decision-making between an Individual and the Group
Authors:	Noriyuki Hatakenaka, Shigemasa Matsuo, Kiriko Sakata and Munehiro Nishida
Abstract:	Crowd behavior depends on social interaction among group members. In particular, there has been considerable interest in the decision-making of such a group on their movement during travel. Here we discuss the decision-making processes in choice selection between two things, i. .e., alternative, by means of numerical simulations based on social force model developed by Helbing et al. This allows us to introduce an individual decision-making process into the decision-making of the whole group through psychological parameter, the so-called dependence p, equivalent to panic parameter in an emergency evacuation. We demonstrate the conflict that arises in the decision-making between an individual and the group in alternative. In addition, we reconfirmed a similar stochastic collective behavior in the decision-making processes observed by Couzin et al. in traveling animals at the large p regimes even if there are no leaders in the group. On the other hand, individualistic behavior is pronounced in smaller p regimes. This feature prevents the formation of group, leading to no collective decision-making anymore. Therefore, the parameter p is a key to consider in the decision-making of both the individual and the group.
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Paper Nr:	132
Title:	A Guidance System for Wide-area Complex Disaster Evacuation based on Ant Colony Optimization
Authors:	Hirotaka Goto, Asuka Ohta, Tomofumi Matsuzawa, Munehiro Takimoto, Yasushi Kambayashi and Masayuki Takeda
Abstract:	This paper reports the results of applying our approach discovering safe evacuation routes to practical situations. Our approach is based on the ant colony optimization (ACO) and it is practical in the light of a real case with a tsunami. ACO have been often employed for finding evacuation routes in traditional approaches, which only take advantage of ants behavior more frequently following traces of other ants’ through pheromone communications. We assume that there are a lot of danger zones in the damaged area. For example Rikuzentakata is a city that extensively damaged in the 2011 Great East Japan Earthquake. In such a case, the traditional approaches may present some unsafe routes through the danger zones. We have proposed an ACO based approach that calculates evacuation routes avoiding danger zones. In our approach, evacuees can deposit deodorant pheromone around danger zones, which makes normal pheromone ineffective, so that our approach gives routes not passing through the danger zones. We have implemented our approach as a simulator, conducting experiments in the same situation as the Rikuzentakata case. Through the results of the experiments, we show that our approach decreases the number of people suffering from collapsed and burning buildings.
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Paper Nr:	137
Title:	An Agent-based Framework for Multi-domain Service Networks - Eduroam Case Study
Authors:	Ameneh Deljoo, Leon Gommans, Tom Van Engers and Cees de Laat
Abstract:	This paper introduces a methodology for the acquisition of the computational model of a service provider group and its transformation into agent-based model. The methodology is as follows. First, we analyze the case at the signal layer, i.e. the message exchange between actors, and model them with the components of “belief, desire and intention (BDI)” agent architecture. In the next step, we identify the implicit actions, intentions, and conditions which are necessary for the story to occur. These steps correspond to descriptions of agent-roles observed in the case study. As a concrete result, a preliminary implementation of the framework has been developed with Groovy.
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Paper Nr:	149
Title:	Semantic Social Network Analysis Foresees Message Flows
Authors:	Matteo Cristani, Claudio Tomazzoli and Francesco Olivieri
Abstract:	Social Network Analysis is employed widely as a means to compute the probability that a given message flows through a social network. This approach is mainly grounded upon the correct usage of three basic graph- theoretic measures: degree centrality, closeness centrality and betweeness centrality. We show that, in general, those indices are not adapt to foresee the flow of a given message, that depends upon indices based on the sharing of interests and the trust about depth in knowledge of a topic. We provide an extended model, that is a simplified version of a more general model already documented in the literature, the Semantic Social Network Analysis, and show that by means of this model it is possible to exceed the drawbacks of general indices discussed above.
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Paper Nr:	150
Title:	Mobile Robotic JChoc DisSolver - A Distributed Constraints Reasoning Platform for Mobile Multi-robot Problems
Authors:	Zakarya Erraji, Mounia Janah, Imade Benelallam and El Houssine Bouyakhf
Abstract:	Due to the computational complexity (NP-Complete) of Constraint Programming (CP), several researchers have abandoned its use in robotic research field. In the last decade, as many approaches of real-time constraint handling have been proposed, constraint programming has proved to be a stand-alone technology that can be used in several research fields. Even if mobile robotics is a complex research area, in this paper, we prove that distributed constraint reasoning techniques can be utilized as a very elegant formalism for multi-robot decision making. First, we describe dynamic distributed constraint satisfaction formalism, the new platform architecture ”RoboChoc” and specify how decision making can be controlled in multi-robots environment using dynamic communication protocols. Then we provide an example application that illustrates how our platform can be used to solve multi-robot problems using constraint programming techniques.
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