mlsanityData to 14 Oct 2026

Monte Carlo Game Solver

arXiv 2001.05087 ↗First posted 15 Jan 2020cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Jan 2020.

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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Study and improvement of search algorithms in two-players perfect information gamescited by 4
  2. 2025On some improvements to Unbounded Minimaxcited by 2
  3. 2025Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problemcited by 0