Fuzzeval: A fuzzy controller-based approach in adaptive learning for backgammon game

Publication Type  Book Article
Year of Publication  2005
Authors  Heinze, M.; Ortiz-Arroyo, D.; Larsen, H. L.; Rodriguez-Henriquez, F.
Secondary/CollectionTitle  MICAI 2005: Advances in Artificial Intelligence
Publisher  Springer-Verlag Berlin
Place Published  Berlin
Pagination  224-233
ISBN Number  0302-9743
Accession Number  ISI:000233852000023
Key Words  fuzzy controller; machine learning; artificial neural networks; computer games; reinforcement learning
Abstract  

In this paper we investigate the effectiveness of applying fuzzy controllers to create strong computer player programs in the domain of backgammon. Fuzzeval, our proposed mechanism, consists of a fuzzy controller that dynamically evaluates the perceived strength of the board configurations it receives. Fuzzeval employs an evaluation function that adjusts the membership functions linked to the linguistic variables employed in the knowledge base. The membership functions are aligned to the average crisp input that was successfully used in the past winning games. Fuzzeval mechanisms are adaptive and have the simplicity associated with fuzzy controllers. Our experiments show that Fuzzeval improves its performance up to 42% after a match of only one hundred backgammon games played against Pubeval, a strong intermediate level program.

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