Admissibility in opponent-model search

  • Authors:

  • H. H. L. M. Donkers;J. W. H. M. Uiterwijk;H. J. van den Herik

  • Affiliations:

  • Department of Computer Science, Institute for Knowledge and Agent Technology (1KAT), Universiteit Maastricht, P.O. Box 616, Maastricht 6200 MD, The Netherlands;Department of Computer Science, Institute for Knowledge and Agent Technology (1KAT), Universiteit Maastricht, P.O. Box 616, Maastricht 6200 MD, The Netherlands;Department of Computer Science, Institute for Knowledge and Agent Technology (1KAT), Universiteit Maastricht, P.O. Box 616, Maastricht 6200 MD, The Netherlands

  • Venue:
  • Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Heuristic search and computer game playing III
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

Opponent-model (OM) search comes with two types of risk. The first type is caused by a player's imperfect knowledge of the opponent, the second type arises from low-quality evaluation functions. In this paper, we investigate the desirability of a precondition, called admissibility, that may prevent the second type of risk. We examine the results of two sets of experiments: the first set is taken from the game of LOA, and the second set from the KQKR chess endgame. The LOA experiments show that when admissibility happens to be absent, the OM results are not positive. The chess experiments demonstrate that when an admissible pair of evaluation functions is available, OM search performs better than minimax, provided that there is sufficient room to make errors. Furthermore, we conclude that the expectation 'the better the quality of the prediction of the opponent's move, the more successful OM search is' is only true if the quality of both evaluation functions is sufficiently high.