Ian Frank

Soccer server: A tool for research on multiagent systems

This article describes Soccer Server, a simulator of the game of soccer designed as a benchmark for evaluating multiagent systems and cooperative algorithms. In real life, successful soccer teams require many qualities, such as basic ball control skills, the ability to carry out strategies, and teamwork. We believe that simulating such behaviors is a significant challenge for computer science, artificial intelligence, and robotics technologies. It is to promote the development of such technologies, and to help define a new standard problem for research, that we have developed Soccer Server. We demonstrate the potential of Soccer Server by reporting an experiment that uses the system to compare the performance of a neural network architecture and a decision tree algorithm at learning the selection of soccer play plans. Other researchers using Soccer Server to investigate the nature of cooperative behavior in a multiagent environment will have the chance to assess their progress at RoboCup-97, an internatio...

Search in games with incomplete information: a case study using Bridge card play

Abstract 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.

MIKE: an automatic commentary system for soccer

This paper describes MIKE, an automatic commentary system for the game of soccer. Since soccer is played by teams, describing the course of a game calls for reasoning about multi-agent interactions. Also, events may occur at any point of the field at any time, making it difficult to fix viewpoints. MIKE interprets this domain with six soccer analysis modules that run concurrently within a role-sharing framework. We describe these analysis modules and also discuss how to control the interaction between them so that an explanation of a game emerges reactively from the system. We present and evaluate examples of the match commentaries produced by MIKE in English, Japanese and French.

Making recipes in the kitchen of the future

INTRODUCTION A kitchen is not just a place of labor. Throughout history, the activity of preparing food has been accompanied (and even used as an excuse for) social interaction and the development of social bonds. Modern lifestyles and convenience foods have reduced the time and effort required for cooking, but at the same time, have lessened the opportunities for interaction. Our contribution is to demonstrate how a “Kitchen of the Future” can use technology to re-introduce such social interactions, and also enable entirely novel forms of communication mediated by computer. Our kitchen supports the automatic generation of web-ready recipe pages, with other possible applications including actual cooking assistance, and communication or education across distances, cultures and generations.

A theoretical and empirical investigation of search in imperfect information games

Abstract We examine search algorithms for games with imperfect information. We first investigate Monte Carlo sampling, showing that for very simple game trees the chance of finding an optimal strategy rapidly approaches zero as size of the tree increases. We identify the reasons for this sub-optimality, and show that the same problems occur in Bridge, a popular real-world imperfect information game. We then analyse the complexity of the underlying problem, proving it to be NP-complete and describing several polynomial time heuristics. We evaluate these heuristics theoretically and experimentally, demonstrating that they significantly out-perform Monte Carlo sampling. Indeed, on a set of Bridge problems drawn from a definitive expert text, our heuristics consistently identify strategies as good as, or superior to, the expert solutions – the first time a game-general tree search algorithm has been capable of such performance.

Automatic Soccer Commentary and RoboCup

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.

The Statistics Proxy Server

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.

Investigating the Complex with Virtual Soccer

We describe Soccer Server, a network-based simulator of soccer that provides a virtual world enabling researchers to investigate the complex system of soccer play. We identify why soccer is such a suitable domain for the creation of this kind of virtual world, and assess how well Soccer Server performs its task. Soccer Server was used in August 1997 to stage the simulation league of the first Robotic Soccer World Cup (RoboCup), held in Nagoya, Japan. This contest attracted 29 software soccer teams, designed by researchers from ten different countries. In 1998, an updated version of Soccer Server will be used to stage the second RoboCup in France, coinciding with the real World Cup of football.

Multi-agent explanation strategies in real-time domains

We examine the benefits of using multiple agents to produce explanations. In particular, we identify the ability to construct prior plans as a key issue constraining the effectiveness of a single-agent approach. We describe an implemented system that uses multiple agents to tackle a problem for which prior planning is particularly impractical: real-time soccer commentary. Our commentary system demonstrates a number of the advantages of decomposing an explanation task among several agents. Most notably, it shows how individual agents can benefit from following different discourse strategies. Further, it illustrates that discourse issues such as controlling interruption, abbreviation, and maintaining consistency can also be decomposed: rather than considering them at the single level of one linear explanation they can also be tackled separately within each individual agent. We evaluate our systems output, and show that it closely compares to the speaking patterns of a human commentary team.

Optimal Play against Best Defence: Complexity and Heuristics

We investigate the best defence model of an imperfect information game. In particular, we prove that finding optimal strategies for this model is NP-complete in the size of the game tree. We then introduce two new heuristics for this problem and show that they outperform previous algorithms. We demonstrate the practical use and effectiveness of these heuristics by testing them on random game trees and on a hard set of problems from the game of Bridge. For the Bridge problem set, our heuristics actually outperform the human experts who produced the model solutions.