用MCTS增强技术提升搜索决策解释力，无需领域知识。
Towards Explaining Monte-Carlo Tree Search by Using Its Enhancements
- 用MCTS增强生成额外数据，实现无知识依赖的解释
- 验证了增强方法能提供更高质量的解释效果
- 适合关注搜索算法可解释性的研究者和开发者
传统可解释人工智能（XAI）多聚焦于特定领域中黑箱模型的通用策略。本文主张在可解释搜索这一子领域中，需采用无知识依赖的解释方法，以阐明智能搜索技术的决策过程。论文提出利用蒙特卡洛树搜索（MCTS）的增强技术作为解决方案，可在不依赖领域知识的前提下获取更多数据并生成更高质量的解释，并分析了主流增强方法所引入的具体类型解释性。目前尚无研究系统探讨MCTS增强的可解释性。本文通过概念验证展示了使用增强技术的优势。
原文摘要 · Abstract (English)
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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