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Cowling, Peter I.; Powley, Edward J.; Whitehouse, Daniel (2012)
Languages: English
Types: Article
Subjects: 1712, 2207, 1702, 2208

Classified by OpenAIRE into

arxiv: Computer Science::Computer Science and Game Theory
Monte Carlo tree search (MCTS) is an AI technique that has been successfully applied to many deterministic games of perfect information. This paper investigates the application of MCTS methods to games with hidden information and uncertainty. In particular, three new information set MCTS (ISMCTS) algorithms are presented which handle different sources of hidden information and uncertainty in games. Instead of searching minimax trees of game states, the ISMCTS algorithms search trees of information sets, more directly analyzing the true structure of the game. These algorithms are tested in three domains with different characteristics, and it is demonstrated that our new algorithms outperform existing approaches to handling hidden information and uncertainty in games. Index Terms—Artificial intelligence (AI), game tree search, hidden information, Monte Carlo methods, Monte Carlo tree search (MCTS), uncertainty.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • Peter I. Cowling (M'05) received the M.A. and D.Phil. degrees from Corpus Christi College, University of Oxford, Oxford, U.K., in 1989 and 1997, respectively. He is a Professor of Computer Science and Associate Dean (Research and Knowledge Transfer) at the University of Bradford, Bradford, U.K., where he leads the ArtiÞcial Intelligence Research Centre. In September 2012, he will take up an Anniversary Chair at the University of York, York, U.K., joined between the Department of Computer Science and
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