Evolving game playing strategies for Othello
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- @InProceedings{Frankland:2015:CEC,
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author = "Clive Frankland and Nelishia Pillay",
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booktitle = "IEEE Congress on Evolutionary Computation (CEC)",
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title = "Evolving game playing strategies for Othello",
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year = "2015",
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pages = "1498--1504",
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isbn13 = "978-1-4799-7491-7",
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abstract = "There has been a fair amount of research into the use
of genetic programming for the induction of game
playing strategies for board games such as chess,
checkers, backgammon and Othello. A majority of this
research has focused on developing evaluation functions
for use with standard game playing algorithms such as
the alpha-beta algorithm or Monte Carlo tree search.
The research presented in this paper proposes a
different approach based on heuristics. Genetic
programming is used to evolve game playing strategies
composed of heuristics. Each evolved strategy
represents a player. While in previous work the game
playing strategies are generally created offline, in
this research learning and generation of the strategies
takes place online, in real time. An initial population
of players created using the ramped half-and-half
method is iteratively refined using reproduction,
mutation and crossover. Tournament selection is used to
choose parents. The board game Othello, also known as
Reversi, is used to illustrate and evaluate this novel
approach. The evolved players were evaluated against
human players, Othello WZebra, AI Factory Reversi and
Math is fun Reversi. This study has revealed the
potential of the proposed novel approach for evolving
game playing strategies for board games. It has also
identified areas for improvement and based on this
future work will investigate mechanisms for
incorporating mobility into the evolved players.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CEC.2015.7257065",
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ISSN = "1089-778X",
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month = may,
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notes = "Also known as \cite{7257065}",
- }
Genetic Programming entries for
Clive Frankland
Nelishia Pillay
Citations