RT Conference Proceedings
T1 Predicting opponent actions by bbservation
A1 Ledezma Espino, Agapito Ismael
A1 Aler, Ricardo
A1 Sanchis de Miguel, María Araceli
A1 Borrajo Millán, Daniel
AB 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.
PB Springer
SN 978-3-540-25046-3
SN 0302-9743 (Print)
SN 1611-3349 (Online)
YR 2004
FD 2004
LK http://hdl.handle.net/10016/5868
UL http://hdl.handle.net/10016/5868
LA eng
DS e-Archivo
RD 30 abr. 2024