Combining Monte-Carlo Tree Search with Proof-Number Search

TL;DR

This paper introduces PN-MCTS, a novel hybrid algorithm combining Proof-Number Search and Monte-Carlo Tree Search, which improves decision-making performance in various games by integrating proof concepts into MCTS.

Contribution

The paper presents a new hybrid tree-search method that incorporates proof and disproof numbers into MCTS, enhancing its effectiveness over basic MCTS.

Findings

PN-MCTS outperforms basic MCTS in multiple games

Achieves win rates up to 94% in tested games

Demonstrates the effectiveness of proof concepts in MCTS

Abstract

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%.