Strategy patterns prediction model (SPPM)

Authors:
Aram B. González;Jorge A. Ramírez Uresti
Affiliations:
Departamento de Tecnologías de Información y Computación, Instituto Tecnologico y de Estudios Superiores de Monterrey, Estado de México, México;Departamento de Tecnologías de Información y Computación, Instituto Tecnologico y de Estudios Superiores de Monterrey, Estado de México, México
Venue:
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Year:
2011

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Abstract

Multi-agent systems are broadly known for being able to simulate real-life situations which require the interaction and cooperation of individuals. Opponent modeling can be used along with multi-agent systems to model complex situations such as competitions like soccer games. In this paper, a model for predicting opponent moves is presented. The model is based around an offline step (learning phase) and an online one (execution phase). The offline step is the one that gets and analyses previous experiences while the online step is the one that uses the data generated by offline analysis to predict opponent moves. This model is illustrated by an experiment with the RoboCup 2D Soccer Simulator.