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arXiv · 2506.13223

Towards Explaining Monte-Carlo Tree Search by Using Its Enhancements

Jakub Kowalski·Mark H. M. Winands·Maksymilian Wiśniewski·Stanisław Reda·Anna Wilbik
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Abstract

Typically, research on Explainable Artificial Intelligence (XAI) focuses on black-box models within the context of a general policy in a known, specific domain. This paper advocates for the need for knowledge-agnostic explainability applied to the subfield of XAI called Explainable Search, which focuses on explaining the choices made by intelligent search techniques. It proposes Monte-Carlo Tree Search (MCTS) enhancements as a solution to obtaining additional data and providing higher-quality explanations while remaining knowledge-free, and analyzes the most popular enhancements in terms of the specific types of explainability they introduce. So far, no other research has considered the explainability of MCTS enhancements. We present a proof-of-concept that demonstrates the advantages of utilizing enhancements.

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Jakub Kowalski, Mark H. M. Winands, Maksymilian Wiśniewski, Stanisław Reda, Anna Wilbik. 2025-06-16. Towards Explaining Monte-Carlo Tree Search by Using Its Enhancements. https://arxiv.org/abs/2506.13223

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