Machine learning methods for opponent modeling in games of imperfect information

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2012

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Şirin, Volkan

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This thesis presents a machine learning approach to the problem of opponent modeling in games of imperfect information. The efficiency of various artificial intelligence techniques are investigated in this domain. A sequential game is called imperfect information game if players do not have all the information about the current state of the game. A very popular example is the Texas Holdem Poker, which is used for realization of the suggested methods in this thesis. Opponent modeling is the system that enables a player to predict the behaviour of its opponent. In this study, opponent modeling problem is approached as a classification problem. An architecture with different classifiers for each phase of the game is suggested. Neural Networks, K-Nearest Neighbors (KNN) and Support Vector Machines are used as classifier. For modeling a particular player, KNN is found to be most successful amongst all, with a prediction accuracy of 88%. An ensemble learning system is proposed for modeling different playing styles and unknown ones. Computational complexity and parallelization of some calculations are also provided.

Subject Keywords

Machine learning., Artificial intelligence., Neural networks (Computer science)., Back propagation (Artificial intelligence)., Computer games., Electronic games.

URI

http://etd.lib.metu.edu.tr/upload/12614630/index.pdf
https://hdl.handle.net/11511/21738

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

V. Şirin, “Machine learning methods for opponent modeling in games of imperfect information,” M.S. - Master of Science, Middle East Technical University, 2012.