Keynote Lectures

Trends and Directions in Distributed Knowledge
Elias M. Awad, University of Virginia, United States

Connecting Sciences
H. Jaap van den Herik, Faculty of Computer Science and Faculty of Law, Leiden University, Netherlands

Control and Delegation
Wiebe van der Hoek, The University of Liverpool, United Kingdom

Abstract

The core of success in today's global commerce is knowledge centricity affecting individuals, organizations, government, and industries alike.  With explicit knowledge tucked away in knowledge bases, a key trend in knowledge transfer (KT) is intrinsic knowledge intensification against cultural, structural, and cognitive barriers.  KT facilitates knowledge exchange via teamwork based on mutual trust, tested integrity, and a shared vision in a knowledge-centric environment.

Networking, per se, facilitates KT but cannot guarantee implementation or integration.  As a result, behavioralists such as knowledge brokers with technology background are slowly being employed to assure connectivity, promote collaboration, instill confidence, enhance communication, and enrich decision-making.  It is a daunting challenge that will take time and experience for KT to take hold.

In the final analysis, KT imperatives must address time management ("when" of KT), HRM ("who" of KT), security management ("why" of KT), task management ("what" knowledge should be transferred), and network management ("how" knowledge should be transferred).  It is an endeavor requiring intelligence, foresight, and leadership in an increasingly complex world.

Abstract
Real progress in science is dependent on novel ideas. In Artificial Intelligence, for a long time (1950-1997) a prevailing question was: can a computer chess program defeat the human World Champion? In 1997 this turned out to be the case by using a supercomputer employing the minimax method with many enhancements, such as opponent modeling. The next question was: can we use similar techniques to outperform the top grandmasters in Go. After some ten years of intensive research the answer was that minimax could not be successfully transferred to the game of Go. In the article, we will review briefly why this transfer failed. Hence, a new method for Go was developed: MCTS (Monte Carlo Tree Search). The results for MCTS in Go are rather promising. At first glance it is quite surprising that MCTS works so well. However, deeper analysis revealed the reasons for this success.
Since the field of AI-research claims to be a fruitful test bed for techniques to be applied in other areas one might look in which areas minimax or MCTS are applicable. A possible and unexpected answer is the application area of solving categories of high energy physics equations. In that area the derivation of the formulas is often performed by the (open source) computer algebra system FORM developed by Jos Vermaseren. The derivation is usually hand guided (human decisions are needed) and becomes difficult when there are many possibilities of which only a few lead to a useful solution. The intriguing question is: can we make the computer select the next step? Our idea is that the next step can be made by using MCTS.
In the article we show the first attempts which prove that this idea is realizable. It implies that games and solving high energy physics equations are connected by MCTS. A first unexpected result is showing the existence of connecting sciences. Equally unexpected is the second result. It is obtained by using MCTS for the improvement of multivariate Horner schemes when converting large formulas to code for numerical evaluation. From the viewpoint of MCTS this is an uncomplicated application and thus it is possible, by varying parameters, to see how MCTS works. It shows that the new ideas can function as cross-fertilization for two, and maybe more, research areas. Hence, the future lies in connecting sciences.

Abstract
The context of the talk is an interest and a need to reason about issues related to cooperation in multi-agent systems, where, given the notion of a coalition (that is, a group of agents), questions arise regarding the coalitional power (what can the coalition achieve?), coalition formation (which coalitions will form?) and the result of cooperation (how will the coalition act?). Coalition Logics provide a tool to analyse some of those questions. They took off with two important developments, namely with Pauly's formulation of Coalition Logic (CL), and the work on Alternating-time Temporal Logic (ATL) by Alur, Henzinger and Kupferman. Basic concept in both systems is the cooperation modality: C phi meaning `coalition C can cooperate to ensure that \phi'. Formally, this is defined to hold as `the members of C can each chose a strategy, such that, no matters what the agents outside C decide to do, phi will be true in all remaining computations' This is the so-called alpha-ability. InCL and ATL however, no answer is given to the question as to where the agents' powers arise from. In our work on Coalition Logic for Proposition Control (CL-PC), we give one possible answer to this: we assume that every agent i is uniquely assigned a set of propositional atoms: the agent has complete control over the truth values. Although ownership of such atoms if fixed is CL-PC, we also look at extensions where we study a notion of delegation, where agents can decide to hand control over certain atoms to others. Finally, we will also look cases where agents have incomplete information about who controls what.