Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Algorithms and assessment in computer poker
Algorithms and assessment in computer poker
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Artificial Intelligence
Poker vision: playing cards and chips identification based on image processing
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
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
Similarity-Based retrieval and solution re-use policies in the game of texas hold'em
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Successful performance via decision generalisation in no limit texas hold'em
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
On combining decisions from multiple expert imitators for performance
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Case-based strategies in computer poker
AI Communications
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
Decision generalisation from game logs in no limit texas Hold'em
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We investigate the use of Monte-Carlo Tree Search (MCTS) within the field of computer Poker, more specifically No-Limit Texas Hold'em. The hidden information in Poker results in so called miximax game trees where opponent decision nodes have to be modeled as chance nodes. The probability distribution in these nodes is modeled by an opponent model that predicts the actions of the opponents. We propose a modification of the standard MCTS selection and backpropagation strategies that explicitly model and exploit the uncertainty of sampled expected values. The new strategies are evaluated as a part of a complete Poker bot that is, to the best of our knowledge, the first exploiting no-limit Texas Hold'em bot that can play at a reasonable level in games of more than two players.