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.

/pdf/multiagent-systems-a-survey-from-a-machine-learning-yps633tity.pdf

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.

/pdf/heads-up-limit-hold-em-poker-is-solved-134wqlsfvm.pdf

Heads-up limit hold’em poker is solved

We examine search algorithms in games with incomplete information, formalising a best defence model of such games based on the assumptions typically made when incomplete information problems are analysed in expert texts. We show that equilibrium point strategies for optimal play exist for this model, and define an algorithm capable of computing such strategies. Using this algorithm as a reference we then analyse search architectures that have been proposed for the incomplete information game of Bridge. These architectures select strategies by analysing some statistically significant collection of complete information sub-games. Our model allows us to clearly state the limitations of such architectures in producing expert analysis, and to precisely formalise and distinguish the problems that lead to sub-optimality. We illustrate these problems with simple game trees and with actual play situations from Bridge itself.

Search in Games with Incomplete Information: A Case Study Using Bridge Card Play

A theoretical and empirical investigation of search in imperfect information games

Introduction.- A good deal of Bridge literature.- Planning literature.- The Bridge search space size.- Proof-planning: solving independent goals using tactics and methods.- Search in games with incomplete information-. Identifying the best strategy: tackling non-locality.- Interleaving plans with dependencies.- Re-introducing neglected actions.- Overall architecture.- Results.- Conclusions.- An overview of commercial computer Bridge systems.- Generating explanations.- Further examples.- User manual.- Code.-

Search and Planning Under Incomplete Information: A Study Using Bridge Card Play

This paper suggests that automated soccer commentary has a key role to play within the overall RoboCup initiative. Firstly, we identify soccer commentary as allowing and requiring investigation of a wide variety of research topics, many of which could not be addressed by the simple development of teams for the RoboCup leagues themselves. Secondly, we highlight a key task of soccer commentary: the expert analysis of a game. We suggest that this expert analysis task has the potential to make a significant impact on RoboCup challenges such as learning, teamwork, and opponent modeling. We illustrate our arguments by discussing the progress on soccer commentary systems to date, in particular reviewing our own system, MIKE.

/pdf/automatic-soccer-commentary-and-robocup-3dv0anv0n9.pdf

Automatic Soccer Commentary and RoboCup

We present a real-time statistical analysis tool for soccer. This system is designed to promote the advancement of RoboCup by facilitating fundamental research on issues such as learning and team evaluation and assessment. Analysis of a game is carried out by a central server, to which clients can connect to request data. We describe the operation of the system and give examples of its potential applications.

Ian Frank

Papers

Search in Games with Incomplete Information: A Case Study Using Bridge Card Play

A theoretical and empirical investigation of search in imperfect information games

Search and Planning Under Incomplete Information: A Study Using Bridge Card Play

Automatic Soccer Commentary and RoboCup

The Statistics Proxy Server