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.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12614630/index.pdf |
| Date | 01 September 2012 |
| Creators | Sirin, Volkan |
| Contributors | Yarman Vural, Fatos Tunay |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for METU campus |
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