Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplines: Distributed Problem Solving (DPS) focuses on the information management aspects of systems with several components working together towards a common goals Multiagent Systems (MAS) deals with behavior management in collections of several independent entities, or agents. This survey of MAS is intended to serve as an introduction to the field and as an organizational framework. A series of general multiagent scenarios are presented. For each scenario, the issues that arise are described along with a sampling of the techniques that exist to deal with them. The presented techniques are not exhaustive, but they highlight how multiagent systems can be and have been used to build complex systems. When options exist, the techniques presented are biased towards machine learning approaches. Additional opportunities for applying machine learning to MAS are highlighted and robotic soccer is presented as an appropriate test bed for MAS. This survey does not focus exclusively on robotic systems. However, we believe that much of the prior research in non-robotic MAS is relevant to robotic MAS, and we explicitly discuss several robotic MAS, including all of those presented in this issue.

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Multiagent Systems: A Survey from a Machine Learning Perspective

This paper provides a comprehensive review of explanations in recommender systems. We highlight seven possible advantages of an explanation facility, and describe how existing measures can be used to evaluate the quality of explanations. Since explanations are not independent of the recommendation process, we consider how the ways recommendations are presented may affect explanations. Next, we look at different ways of interacting with explanations. The paper is illustrated with examples of explanations throughout, where possible from existing applications.

A Survey of Explanations in Recommender Systems

RoboCup simulated soccer presents many challenges to reinforcement learning methods, including a large state space, hidden and uncertain state, multiple independent agents learning simultaneously, and long and variable delays in the effects of actions. We describe our application of episodic SMDP Sarsa(λ) with linear tile-coding function approximation and variable λ to learning higher-level decisions in a keepaway subtask of RoboCup soccer. In keepaway, one team, “the keepers,” tries to keep control of the ball for as long as possible despite the efforts of “the takers.” The keepers learn individually when to hold the ball and when to pass to a teammate. Our agents learned policies that significantly outperform a range of benchmark policies. We demonstrate the generality of our approach by applying it to a number of task variations including different field sizes and different numbers of players on each team.

/pdf/reinforcement-learning-for-robocup-soccer-keepaway-ku565uhymb.pdf

Reinforcement learning for RoboCup soccer keepaway

Games and Decisions: Introduction and Critical Survey.

Poker is a family of games that exhibit imperfect information, where players do not have full knowledge of past events. Whereas many perfect-information games have been solved (e.g., Connect Four and checkers), no nontrivial imperfect-information game played competitively by humans has previously been solved. Here, we announce that heads-up limit Texas hold’em is now essentially weakly solved. Furthermore, this computation formally proves the common wisdom that the dealer in the game holds a substantial advantage. This result was enabled by a new algorithm, CFR + , which is capable of solving extensive-form games orders of magnitude larger than previously possible.

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Heads-up limit hold’em poker is solved

We believe that Internet and computing technology have reached the stage where online games can offer an experience that is qualitatively different to what has gone before. To test how exposure to modern games could be of benefit to players, we recruited 12 subjects to play the MMORPG (Massively Multiplayer Online Role Playing Game) "Nobunaga no Yabou Online", collecting data over two months. We evaluated subjects before and after playing, using psychological tests and tests of communication skill, typing skill and knowledge of the Internet and of online games. While the psychological tests showed no significant changes in the subjects' state of mind, there were measurable improvements in online communication skill, typing skill and knowledge, with the largest increases experienced by the subjects with least Internet experience.

Some positive effects of online gaming

Learning from the Koto Wedge: The Sphere and the Shawl

Proof Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Tristan Cazenave Solving Kriegspiel-Like Problems: Examining Efficient Search Methods . . . 55 Makoto Sakuta and Hiroyuki Iida Strategies for the Automatic Construction of Opening Books . . . . . . . . . . . . 74 Thomas R. Lincke Awari Retrograde Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Roel van der Goot Construction of Chinese Chess Endgame Databases by Retrograde Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Haw-ren Fang, Tsan-sheng Hsu, and Shun-chin Hsu Part 2: Learning and Pattern Acquisition Learning from Perfection. A Data Mining Approach to Evaluation Function Learning in Awari . . . . . . 115 Jack van Rijswijck Chess Neighborhoods, Function Combination, and Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Robert Levinson and Ryan Weber Learning a Go Heuristic with Tilde . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Jan Ramon, Tom Francis, and Hendrik Blockeel Learning Time Allocation Using Neural Networks . . . . . . . . . . . . . . . . . . . . . . 170 Levente Kocsis, Jos Uiterwijk, and Jaap van den Herik Part 3: Theory and Complexity Issues The Complexity of Graph Ramsey Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 Wolfgang Slany XII Table of

Computers and Games

We present a novel multi-robot system capable of performing cheerleader dances. Our team of seven robots are constructed from very simple LEGO Mindstorms kits, yet still monitor their own synchronisation automatically in real time. We describe how we use a scripting language to abstract the overall control into a set of higher-order functions, and to produce fault tolerancy.

/pdf/lego-mindstorms-cheerleading-robots-2yt6kl5qy4.pdf

Lego mindstorms cheerleading robots

We address the problem of information flow in disaster relief scenarios by presenting an architecture for generating natural language dialogues between large numbers of agents This architecture is the first step towards real-time support systems for relief workers and their controllers Our work demonstrates how techniques from the MIKE commentary system for RoboCup soccer can be carried over to the domain of RoboCup Rescue Thanks to this background, the initial product of our research is a system that explains a RoboCup Rescue simulation not to the agents in the domain themselves but to a watching audience This "commentary" is produced by recreating the actual dialogues most likely be occurring in the domain: walkie-talkie conversations

Ian Frank

Papers

Some positive effects of online gaming

Learning from the Koto Wedge: The Sphere and the Shawl

Computers and Games

Lego mindstorms cheerleading robots

Walkie-Talkie MIKE