The Winning Advantage: Using Opponent Models in Robot Soccer

Abstract

Abstract. Opponent modeling is a skill in multi-agent systems (MAS) which attempts to create a model of the behavior of the opponent. This model can be used to predict the future actions of the opponent and generate appropriate strategies to play against it. Several researches present different methods to create an opponent model in the RoboCup environment. However, how these models can impact the performance of teams is an essential aspect. This paper introduces a novel approach to use efficiently opponent models in order to improve our own team behavior. The basis of this approach is the research done by CAOS Coach Team for modeling and recognizing behaviors evaluated in the RoboCup Coach Competition 2006.For using these models, it is necessary a special agent (coach) which can model the observed opponent team (based on the previous research) and communicate a counter-strategy to the coached players (using the approach proposed in this paper). The evaluation of this approach is a hard problem, but we have conducted several experiments that can help us to know if we are going in a promising direction.

Improving a simulated soccer team's performance through a Memory-Based Collaborative Filtering approach

“…These studies involved 2 phases: an offline phase where the intention was to model the behavior of the opponent; and an online phase where the aim was to classify the opponent [7] based on the assumption that to adopt the best strategy for a given opponent one must use some previous knowledge. This approach has also been used by other authors [8][9][10] and it is justified by the fact that a soccer game has a limited and short duration. However, this approach cannot adapt to changes in the strategy of the opponent team that occur during the match and consequently will most likely lead to non-optimal results in these circumstances.…”