Towards Explaining Monte-Carlo Tree Search by Using Its Enhancements

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.

@article{kowalski2025_2506.13223,
  title={ Towards Explaining Monte-Carlo Tree Search by Using Its Enhancements },
  author={ Jakub Kowalski and Mark H. M. Winands and Maksymilian Wiśniewski and Stanisław Reda and Anna Wilbik },
  journal={arXiv preprint arXiv:2506.13223},
  year={ 2025 }
}