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Information Set Monte Carlo Tree Search

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JournalComputational Intelligence and AI in Games, IEEE Transactions on
DatePublished - 2012
Issue number2
Volume4
Number of pages24
Pages (from-to)120-143
Original languageEnglish

Abstract

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.

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