Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.539197
Title: Investigating evolutionary checkers by incorporating individual and social learning, N-tuple systems and a round robin tournament
Author: Al-Khateeb, Belal
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2011
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Abstract:
In recent years, much research attention has been paid to evolving self-learning game players. Fogel's Blondie24 is just one demonstration of a real success in this field and it has inspired many other scientists. In this thesis, artificial neural networks are employed to evolve game playing strategies for the game of checkers by introducing a league structure into the learning phase of a system based on Blondie24. We believe that this helps eliminate some of the randomness in the evolution. The best player obtained is tested against an evolutionary checkers program based on Blondie24. The results obtained are promising. In addition, we introduce an individual and social learning mechanism into the learning phase of the evolutionary checkers system. The best player obtained is tested against an implementation of an evolutionary checkers program, and also against a player, which utilises a round robin tournament. The results are promising. N-tuple systems are also investigated and are used as position value functions for the game of checkers. The architecture of the n-tuple is utilises temporal difference learning. The best player obtained is compared with an implementation of evolutionary checkers program based on Blondie24, and also against a Blondie24 inspired player, which utilises a round robin tournament. The results are promising. We also address the question of whether piece difference and the look-ahead depth are important factors in the Blondie24 architecture. Our experiments show that piece difference and the look-ahead depth have a significant effect on learning abilities.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.539197  DOI: Not available
Keywords: Q Science (General)
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