Abstract
As virtual environments are becoming graphically nearly realistic, the need for a satisfying Artificial Intelligence (AI) is perceived as more and more important by game players. In particular, what players have to face nowadays in terms of AI is not far from what was available at the beginning of the video games era. Even nowadays, the AI of almost all games is based on a finite set of actions/reactions whose sequence can be easily predicted by expert players. As a result, the game soon becomes too obvious to still be fun. Instead, machine learning techniques could be employed to classify a player’s behavior and consequently adapt the game’s AI; the competition against the AI would become more stimulant and the fun of the game would last longer. To this aim, we consider a game where both the player and the AI have a limited information about the current game state and where it is part of the game to guess the information hidden by the opponent. We demonstrate how machine learning techniques could be easily implemented in this context to improve the AI by making it adaptive with respect to the strategy of a specific player.
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References
Allis, V. (1988). A knowledge-based approach of Connect-Four the game is solved: White wins. Masters Thesis, Department of Mathematics and Computer Science, Vrije Universiteit, Amsterdam, Netherlands.
Billings, D., Pena L., Schaeffer, J., Szafron, D. (1999). Using probabilistic knowledge and simulation to play Poker. In Proceedings of the 16th National Conference on Artificial Intelligence (AAAI-99), Orlando, Florida, 697-703.
Billings, D. (2000). The first international RoShamBo programming competition. International Computer Games Association Journal 23(1), 42-50.
Bud, A., Albrecht, D., Nicholson, A., Zukerman, I. (2001). Playing Invisible Chess with Information-Theoretic Advisors. In Proc. 2001 AAAI Spring Symposium on Game Theoretic and Decision Theoretic Agents, California, USA, 6-15.
Buro, M. (1997). The Othello match of the year: Takeshi Murakami vs. Logistello. International Computer Chess Association Journal 20(3), 189-193.
Campbell, M. S. (1999). Knowledge discovery in Deep Blue. Communications of the ACM, 42(11), 65-67.
Ciancarini, P., Dalla Libera, F., Maran, F. (1997). Decision Making under Uncertainty: A Rational Approach to Kriegspiel. In J. van den Herik and J. Uiterwijk, editors, Advances in Computer Chess 8, 277-298.
Egnor, D. (2000). Iocaine powder. International Computer Games Association Journal 23(1), 33-35.
Gasser, R. (1995). Efficiently harnessing computational resources for exhaustive search. Ph. D. thesis, ETH, Zurich, Switzerland.
Ginsberg, M. L. (1999). GIB: Steps toward an expert-level Bridge-playing program. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-99), Stockholm, Sweden, 584-589.
Mitchell T. (1997). Machine learning. McGraw Hill.
Samuel, A. L. (1959). Some studies in machine learning using the game of Checkers. IBM Journal of Research and Development 3(3), 211-229.
Schaeffer, J. (2000). The games computers (and people) play. InM. V. Zelkowitz (Ed.), Advances in Computers, 50, 189-266.
Sheppard B. (1999). Mastering Scrabble. IEEE Intelligent Systems 14(6), 15-16.
Vapnik V. (1995). The nature of statistical learning theory. Springer-Verlag.
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© 2008 IFIP International Federation for Information Processing
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Aiolli, F., Palazzi, C.E. (2008). Enhancing Artificial Intelligence in Games by Learning the Opponent’s Playing Style. In: Ciancarini, P., Nakatsu, R., Rauterberg, M., Roccetti, M. (eds) New Frontiers for Entertainment Computing. ECS 2008. IFIP International Federation for Information Processing, vol 279. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09701-5_1
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DOI: https://doi.org/10.1007/978-0-387-09701-5_1
Publisher Name: Springer, Boston, MA
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