Abstract
Computer game-playing is a challenging topic in artificial intelligence. The recent results by the computer programs Deep Blue (1996, 1997) and Deep Junior (2002) against Kasparov show the power of current game-tree search algorithms in Chess. This success is owed to the fruitful combination of the theoretical development of algorithms and their practical application. As an example of the theoretical development we discuss a game-tree algorithm called Opponent-Model search. In contrast to most current algorithms, this algorithm uses an opponent model to predict the opponent’s moves and uses these predictions to lure the opponent into uncomfortable positions. We concentrate on the time complexity of two different implementations of the algorithm and show how these are derived. Moreover, we discuss some possible dangers when applying Opponent-Model search in practice.
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References
Schaeffer, J., van den Herik, H.J. (eds.): Chips Challenging Champions. Games, Computers and Artificial Intelligence. Elsevier Science Publishers, Amsterdam (2002)
van den Herik, H.J.: Informatica en het menselijk blikveld. Inaugural Address. University of Limburg, Maastricht (1988) (in Dutch)
Carmel, D., Markovitch, S.: Learning models of opponent’s strategies in game playing. In: Proceedings AAAI Fall Symposion on Games: Planning and Learning, Raleigh, NC, pp. 140–147 (1993)
Iida, H., Uiterwijk, J.W.H.M., van den Herik, H.J.: Opponent-model search. Technical Report CS 93-03, Universiteit Maastricht, Maastricht, The Netherlands (1993)
Iida, H., Uiterwijk, J.W.H.M., van den Herik, H.J., Herschberg, I.S.: Potential applications of opponent-model search. Part 1, the domain of applicability. ICCA Journal 16(4), 201–208 (1993); Part 2, risks and strategies. ICCA Journal 17(1), 10–14 (1994)
Carmel, D., Markovitch, S.: Pruning algorithms for multi-model adversary search. Artificial Intelligence 99, 325–255 (1998)
Donkers, H.H.L.M.: Nosce Hostem – Searching With Opponent Models. PhD thesis, Universieit Maastricht, Maastricht, The Netherlands (2003)
Iida, H., Kotani, I., Uiterwijk, J.W.H.M., van den Herik, H.J.: Gains and risks of OM Search. In: van den Herik, H.J., Uiterwijk, J.W.H.M. (eds.) Advances in Computer Chess 8, Maastricht, The Netherlands, Universiteit Maastricht, pp. 153–165 (1997)
Donkers, H.H.L.M., Uiterwijk, J.W.H.M., van den Herik, H.J.: Admissibility in opponent-model search. Information Sciences 154(3–4), 119–140 (2003)
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© 2004 Springer-Verlag Berlin Heidelberg
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van den Herik, H.J., Donkers, H.H.L.M. (2004). Games, Theory and Applications. In: Van Emde Boas, P., Pokorný, J., Bieliková, M., Štuller, J. (eds) SOFSEM 2004: Theory and Practice of Computer Science. SOFSEM 2004. Lecture Notes in Computer Science, vol 2932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24618-3_1
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DOI: https://doi.org/10.1007/978-3-540-24618-3_1
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