Opponent Modeling in RoboCup Soccer Simulation 3D

Abstract

Opponent modeling [1] is a research area that focuses on the analysis and interpretation of adversary’s actions. This modeling, although, is easy for humans but is a challenging task for autonomous multi-agents [2]. In a multi-agent environment, an opponent typically represents a team of agents who are capable of doing decentralized decision making. Thus, an opponent model consists of a collective set of tactical behaviors, termed as strategy, that are exhibited by agents in a dynamic and uncertain environment. RoboCup Soccer [3] provides such an environment and serves as a test bed for analysis and application of new intelligent algorithms. In the context of RoboCup Soccer Simulation League 2D, opponent modeling has been exhaustively researched and various machine learning techniques have been devised for strategy prediction and identification. However, when it comes to RoboCup Soccer Simulation League 3D the problem is much more sophisticated as identification of basic actions that are being performed by a single agent is itself a complex task. This research aims to develop a framework that takes a team of agents as input, analyzes their set of individual actions as well as coordinated team behavior using a set of pre-defined rules, interprets their strategic pattern and suggests a counter strategy. The framework would involve both offline and online learning. The major benefit of this approach is that if we are able to judge our opponent within the first thirty seconds (or so) then we can apply specific rules for exclusively dealing with it. For example, certain teams make excessive use of kicking the ball while many others have the tendency to dribble the ball to maintain possession of it. If the opponent has strong dribbling skill then our best strategy would be to block the path of the opponent and put most of our players to this job. On the other hand, if the opponent team has a tendency to frequently kick the ball then we would like to keep most of our players around our goal area in order to save the goal (especially if the ball is in our half). Thus, if we are able to classify our opponent based upon certain key attributes then we will be able to devise opponent-specific strategy. The motivation behind this approach is that our team KarachiKoalas [4] is applying this technique and utilizing the features obtained from offline team analysis to build a model of the opponent team within initial seconds of the game.