通过利用蒙特卡洛树搜索的增强技术来解释蒙特卡洛树搜索
Towards Explaining Monte-Carlo Tree Search by Using Its Enhancements
AI总结：
该研究针对可解释搜索领域缺乏知识无关解释方法的问题，提出利用MCTS增强技术在无知识依赖下获取高质量解释，并通过概念验证证明了其优势，填补了MCTS增强技术可解释性研究的空白。
摘要：
可解释人工智能（XAI）领域的研究通常聚焦于已知特定领域内通用策略下的黑盒模型。本文提出需将知识无关的可解释性应用于XAI的子领域——可解释搜索，该子领域专注于解释智能搜索技术做出的选择。文章提出以蒙特卡洛树搜索（MCTS）的增强技术为解决方案，在保持无知识依赖的前提下获取额外数据并提供更高质量的解释，同时针对各类增强技术引入的具体可解释性类型，分析了当前最流行的几种增强方法。目前尚无其他研究探讨过MCTS增强技术的可解释性。我们提出了一个概念验证，展示了利用这些增强技术的优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
