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Title: Monte-Carlo tree search in poker using expected reward distributions
Authors: Van den Broeck, Guy ×
Driessens, Kurt
Ramon, Jan #
Issue Date: 30-Oct-2009
Host Document: Proceedings of the 21st Benelux Conference on Artificial Intelligence
Conference: Benelux Conference on Artificial Intelligence (BNAIC) edition:21 location:Eindhoven, the Netherlands date:29-30 October 2009
Article number: 85
Abstract: 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 mixi-max 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.
ISSN: 1568-7805
Publication status: published
KU Leuven publication type: IMa
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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