Publication: Predicting opponent actions by bbservation
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ISSN: 0302-9743 (Print)
ISSN: 1611-3349 (Online)
ISBN: 978-3-540-25046-3
Publication date
2004
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Springer
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Abstract
In competitive domains, the knowledge about the opponent can give players a clear advantage. This idea lead us in the past to propose an approach to acquire models of opponents, based only on the observation of their input-output behavior. If opponent outputs could be accessed directly, a model can be constructed by feeding a machine learning method with traces of the opponent. However, that is not the case in the Robocup domain. To overcome this problem, in this paper we present a three phases approach to model low-level behavior of individual opponent agents. First, we build a classifier to label opponent actions based on observation. Second, our agent observes an opponent and labels its actions using the previous classifier. From these observations, a model is constructed to predict the opponent actions. Finally, the agent uses the model to anticipate opponent reactions. In this paper, we have presented a proof-of-principle of our approach, termed OMBO (Opponent Modeling Based on Observation), so that a striker agent can anticipate a goalie. Results show that scores are significantly higher using the acquired opponentrsquos model of actions.
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Bibliographic citation
Proceedings of: RoboCup 2004, Robot Soccer World Cup VIII, vol. 3276, p. 286-297