Monte Carlo Game Solver

2020/01/15 by Tristan Cazenave, Cazenave, Tristan · 3 citations
Computer Science · Economics, Econometrics and Finance · Psychology · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Educational Games and Gamification #FOS: Computer and information sciences #Sports Analytics and Performance #cs.AI

paper · pdf · doi:10.48550/arxiv.2001.05087

arxiv created 2020/01/15 · openalex publication_date 2020/01/15 · arxiv updated 2020/01/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a general algorithm to order moves so as to speedup exact game solvers. It uses online learning of playout policies and Monte Carlo Tree Search. The learned policy and the information in the Monte Carlo tree are used to order moves in game solvers. They improve greatly the solving time for multiple games.

Cited by

• On some improvements to Unbounded Minimax
• Study and improvement of search algorithms in two-players perfect information games
• Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem

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• Proof Number Based Monte-Carlo Tree Search
• Combining Monte-Carlo Tree Search with Proof-Number Search
• Monte Carlo Permutation Search
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• Simplifications to Guide Monte Carlo Tree Search in Combinatorial Games