Evolving game playing strategies for Othello

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Frankland:2015:CEC,
  author =       "Clive Frankland and Nelishia Pillay",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Evolving game playing strategies for Othello",
  year =         "2015",
  pages =        "1498--1504",
  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.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2015.7257065",
  ISSN =         "1089-778X",
  month =        may,
  notes =        "Also known as \cite{7257065}",
}

Genetic Programming entries for Clive Frankland Nelishia Pillay

Citations