Learning the risk board game with classifier systems

Authors:
Atila Neves;Osvaldo Brasāo;Agostinho Rosa
Affiliations:
LaSEEB-ISR-IST, The Technical University of Lisbon, Av.Rovisco Pais, 1-Torre Norte,6.21, 1049-001 LISBOA, PORTUGAL;LaSEEB-ISR-IST, The Technical University of Lisbon, Av.Rovisco Pais, 1-Torre Norte,6.21, 1049-001 LISBOA, PORTUGAL;LaSEEB-ISR-IST, The Technical University of Lisbon, Av.Rovisco Pais, 1-Torre Norte,6.21, 1049-001 LISBOA, PORTUGAL
Venue:
Proceedings of the 2002 ACM symposium on Applied computing
Year:
2002

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Abstract

The goal is to produce agents that are able to play the board game efficiently. Classifier Systems (CS) were chosen to learn the task at hand. CS were used to learn how to classify a set of (state, action) pairs. These pairs represent a game situation and the action a sensible player should execute when faced with such a situation. Results show that the CS agents perform poorly when compared to humans, but can hold their own in specific situations against computer agents with a fixed, pre-programmed strategy.