Investigating evolutionary checkers by incorporating individual and social learning, N-tuple systems and a round robin tournament

Al-Khateeb, Belal (2011) Investigating evolutionary checkers by incorporating individual and social learning, N-tuple systems and a round robin tournament. PhD thesis, University of Nottingham.

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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.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Kendall, G.
Subjects: Q Science > Q Science (General)
Faculties/Schools: UK Campuses > Faculty of Science > School of Computer Science
Item ID: 12267
Depositing User: EP, Services
Date Deposited: 02 Dec 2011 09:23
Last Modified: 17 Sep 2016 11:26
URI: http://eprints.nottingham.ac.uk/id/eprint/12267

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