Combining Monte-Carlo tree search with proof-number search
E Doe, MHM Winands, DJNJ Soemers… - … IEEE Conference on …, 2022 - ieeexplore.ieee.org
2022 IEEE Conference on Games (CoG), 2022•ieeexplore.ieee.org
Proof-Number Search (PNS) and Monte-Carlo Tree Search (MCTS) have been successfully
applied for decision making in a range of games. This paper proposes a new approach
called PN-MCTS that combines these two tree-search methods by incorporating the concept
of proof and disproof numbers into the UCT formula of MCTS. Experimental results
demonstrate that PN-MCTS outperforms basic MCTS in several games including Lines of
Action, MiniShogi, Knightthrough, and Awari, achieving win rates up to 94.0%.
applied for decision making in a range of games. This paper proposes a new approach
called PN-MCTS that combines these two tree-search methods by incorporating the concept
of proof and disproof numbers into the UCT formula of MCTS. Experimental results
demonstrate that PN-MCTS outperforms basic MCTS in several games including Lines of
Action, MiniShogi, Knightthrough, and Awari, achieving win rates up to 94.0%.
Proof-Number Search (PNS) and Monte-Carlo Tree Search (MCTS) have been successfully applied for decision making in a range of games. This paper proposes a new approach called PN-MCTS that combines these two tree-search methods by incorporating the concept of proof and disproof numbers into the UCT formula of MCTS. Experimental results demonstrate that PN-MCTS outperforms basic MCTS in several games including Lines of Action, MiniShogi, Knightthrough, and Awari, achieving win rates up to 94.0%.
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