Proof Number Based Monte-Carlo Tree Search

This paper proposes a new game-search algorithm, PN-MCTS, which combines Monte-Carlo Tree Search (MCTS) and Proof-Number Search (PNS). These two algorithms have been successfully applied for decision making in a range of domains. We define three areas where the additional knowledge provided by the p...

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Bibliographic Details
Published in:IEEE transactions on games 2024, p.1-10
Main Authors: Kowalski, Jakub, Doe, Elliot, Winands, Mark H. M., Gorski, Daniel, Soemers, Dennis J. N. J.
Format: Article
Language:English
Subjects:
Backpropagation
Games
MCTS Solver
Monte Carlo methods
monte-carlo tree search
proof-number search
Search methods
Task analysis
Uncertainty
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Summary:This paper proposes a new game-search algorithm, PN-MCTS, which combines Monte-Carlo Tree Search (MCTS) and Proof-Number Search (PNS). These two algorithms have been successfully applied for decision making in a range of domains. We define three areas where the additional knowledge provided by the proof and disproof numbers gathered in MCTS trees might be used: final move selection, solving subtrees, and the UCB1 selection mechanism. We test all possible combinations on different time settings, playing against vanilla UCT on several games: Lines of Action (7×7 and 8×8 board sizes), MiniShogi, Knightthrough, and Awari. Furthermore, we extend this new algorithm to properly address games with draws, like Awari, by adding an additional layer of PNS on top of the MCTS tree. The experiments show that PN-MCTS is able to outperform MCTS in all tested game domains, achieving win rates up to 96.2% for Lines of Action.
ISSN:2475-1502
2475-1510
DOI:10.1109/TG.2024.3403750