Abstract
Opponent-Model search is a game-tree search method that explicitly uses knowledge of the opponent. There is some risk involved in using Opponent-Model search. Both the prediction of the opponent’s moves and the estimation of the profitability of future positions should be of good quality and as such they should obey certain conditions. To investigate the role of prediction and estimation in actual computer game-playing, experiments with Opponent-Model search were performed in the game of Bao. After five evaluation functions had been generated using machine-learning techniques, a series of tournaments between these evaluation functions was executed. They showed that Opponent-Model search can be applied successfully, provided that the conditions are met.
Chapter PDF
Similar content being viewed by others
References
Baxter, J., Trigdell, A., and Weaver, L. (1998). KNIGHTCAP: a Chess Program that Learns by Combining TD(À) with Game-Tree Search. Proc. 15th International Conf on Machine Learning, pp. 28–36, Morgan Kaufmann, San Francisco, CA.
Carmel, D. and Markovitch, S. (1993). Learning Models of Opponent’s Strategies in Game Playing. Proceedings AAAI Fall Symposion on Games: Planning and Learning, pp. 140–147, Raleigh, NC.
Carmel, D. and Markovitch, S. (1998). Pruning Algorithms for Multi-Model Adversary Search. Artificial Intelligence, Vol. 99, No. 2, pp. 325–355.
Donkers, H. H. L. M. and Uiterwijk, J. W. H. M. (2002). Programmming Bao. Seventh Computer Olympiad: Computer-Games Workshop Proceedings (ed. J. W. H. M. Uiterwijk), Technical Report CS 02–03, Universiteit Maastricht, Maastricht, The Netherlands.
Donkers, H. H. L. M., Uiterwijk, J. W. H. M., and Herik, H. J. van den (2001). Probabilistic Opponent-Model Search. Information Sciences, Vol. 135, pp. 123–149.
Donkers, H. H. L. M., Uiterwijk, J. W. H. M., and Voogt, A. J. de (2002). Mancala Games — topics in Artificial Intelligence and Mathematics. Step by Step. Proceedings of the 4th Colloquium `Board Games in Academia’ (eds. J. Retschitzki and R. Haddad-Zubel), Editions Universitaires, Fribourg, Switserland.
Donkers, H. H. L. M., Uiterwijk, J. W. H. M., and Herik, H. J. van den (2003). Admissibility in Opponent-Model Search. Information Sciences, Vol. 154, Nos. 3–4, pp. 195–202.
Herik, H. J. van den, Uiterwijk, J. W. H. M., and Rijswijck, J. van (2002). Games Solved: Now and in the Future. Artificial Intelligence, Vol. 134, Nos. 1–2, pp. 277–311.
Holland, J. H. (1975). Adaption in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI.
Iida, H., Uiterwijk, J. W. H. M., and Herik, H. J. van den (1993). Opponent-Model Search. Technical Report CS 93–03, Universiteit Maastricht, Maastricht, The Netherlands.
Iida, H., Handa, K.-i., and Uiterwijk, J. W. H. M. (1995). Tutoring Strategies in Game-Tree Search. ICCA Journal, Vol. 18, No. 4, pp. 191–204.
Murray, H. J. R. (1952). A History of Board Games other than Chess. Oxford University Press, Oxford, UK.
Russ, L. (2000). The Complete Mancala Games Book. Marlow & Company, New York, NY. Voogt, A. J. de (1995). Limits of the Mind. Towards a Characterisation of Bao Mastership. Ph.D. thesis, University of Leiden, The Netherlands.
Yoshioka, T., Ishii, S., and Ito, M. (1999). Strategy Acquisition for the Game Othello Based on Reinforcement Learning. IEICE Transactions on Information and Systems, Vol. E82-D, No. 12, pp. 1618–1626.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 IFIP International Federation for Information Processing
About this chapter
Cite this chapter
Donkers, H.H.L.M., van den Herik, H.J., Uiterwijk, J.W.H.M. (2004). Opponent-Model Search in Bao: Conditions for a Successful Application. In: Van Den Herik, H.J., Iida, H., Heinz, E.A. (eds) Advances in Computer Games. IFIP — The International Federation for Information Processing, vol 135. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35706-5_20
Download citation
DOI: https://doi.org/10.1007/978-0-387-35706-5_20
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-4424-8
Online ISBN: 978-0-387-35706-5
eBook Packages: Springer Book Archive