An Experimental Approach to Online Opponent Modeling in Texas Hold'em Poker
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Monte-Carlo Tree Search in Poker Using Expected Reward Distributions
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Artificial Intelligence
Building a no limit texas hold'em poker agent based on game logs using supervised learning
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Exploiting adversary's risk profiles in imperfect information security games
GameSec'11 Proceedings of the Second international conference on Decision and Game Theory for Security
Computing approximate Nash Equilibria and robust best-responses using sampling
Journal of Artificial Intelligence Research
Repeated zero-sum games with budget
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
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The game of poker offers a clean well-defined domain in which to investigate some truly fundamental issues in computing science, such as how to handle deliberate misinformation, and how to make intelligent guesses based on partial knowledge. In the taxonomy of games, poker lies at the opposite end of the spectrum from well-studied board games like checkers and chess. Poker is a multi-player game with stochasticity (random events occurring over a known probability distribution), imperfect information (some information is hidden from each player), and partially observable outcomes (some information might never be known). Consequently, the use of deception, opponent modeling, and coping with uncertainty are indispensable elements of high-level strategic play. Traditional methods for computer game-playing are incapable of handling these properties. Writing a program to play a skillful game of poker is a challenging proposition from an engineering perspective as well, since each decision must be made quickly (typically about one second). A major theme of this dissertation is the evolution of architectures for poker-playing programs that has occurred since the research began in 1992. Four distinct approaches we address are: knowledge-based systems, simulation, game-theoretic methods, and adaptive imperfect information game-tree search. Each phase of the research explored the strengths and weaknesses of the corresponding architectures, both in theory and in practice. The important problem of obtaining an accurate assessment of performance is also addressed. The creation of a powerful tool for this purpose, called DIVAT, is discussed in detail. The academic papers arising from each of these studies constitute the core chapters of this thesis. The conclusion discusses some of the advantages and shortcomings of each approach, along with the most valuable lessons learned in the process. Although the goal of surpassing all human players has not yet been attained, the path has been cleared. The best poker programs are beginning to pose a serious threat, even in this most "human" of games. As programs continue to improve, they provide new insights into poker strategy, and valuable lessons on how to handle various forms of uncertainty.