Using Genetic Programming to Evolve Board Evaluation Functions for a Boardgame   [GP]

by

Ferrer, G., J. and Martin, W., N.

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Info: 1995 IEEE Conference on Evolutionary Computation (Conference proceedings), 1995, p. 747
Keywords:genetic algorithms, genetic programming, Senet
Abstract:
In this paper, we employ the genetic programming paradigm to [GP] enable a computer to learn to play strategies for the ancient Egyptian boardgame Senet by evolving board evaluation functions. Formulating the problem in terms of board evaluation functions made it feasible to evaluate the fitness of game playing strategies [GP] by using tournament-style fitness evaluation. [FE] The game has elements of both strategy and chance. Our approach learns strategies which enable the computer to play consistently at a reasonably skillful level.
Notes:
ICEC-95 http://www.io.org/~causal/c_p/cpec95.htm Editors not given by IEEE, Organisers David Fogel and Chris deSilva. conference details at http://ciips.ee.uwa.edu.au/~dorota/icnn95.html Fitness given by knockout tournament, rank-proprtionate selection, mutation and crossover, generational, non-standard random initial population creation/mutation/crossover, no size limit on programs. 2 non-seeded runs, 2 seeded runs (504 random + 8 different hand-coded). No discussion of statistical significance of results.
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BibTex:
@InProceedings{ferrer:1995:bef,
  author =       "Gabriel J. Ferrer and Worthy N. Martin",
  title =        "Using Genetic Programming to Evolve Board Evaluation
                 Functions for a Boardgame",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "2",
  pages =        "747",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Senet",
  URL =          "http://www.cs.virginia.edu/~gjf2a/work/papers/senet.ps",
  url_2 =        "ftp://cs.ucl.ac.uk/genetic/papers/senet.ps.gz",
  size =         "6 pages",
  abstract =     "In this paper, we employ the genetic programming
                 paradigm to enable a computer to learn to play
                 strategies for the ancient Egyptian boardgame Senet by
                 evolving board evaluation functions. Formulating the
                 problem in terms of board evaluation functions made it
                 feasible to evaluate the fitness of game playing
                 strategies by using tournament-style fitness
                 evaluation. The game has elements of both strategy and
                 chance. Our approach learns strategies which enable the
                 computer to play consistently at a reasonably skillful
                 level.",
  notes =        "ICEC-95 http://www.io.org/~causal/c_p/cpec95.htm
                 Editors not given by IEEE, Organisers David Fogel and
                 Chris deSilva.

                 conference details at
                 http://ciips.ee.uwa.edu.au/~dorota/icnn95.html

                 Fitness given by knockout tournament, rank-proprtionate
                 selection, mutation and crossover, generational,
                 non-standard random initial population
                 creation/mutation/crossover, no size limit on programs.
                 2 non-seeded runs, 2 seeded runs (504 random + 8
                 different hand-coded). No discussion of statistical
                 significance of results.

                 ",
}