发表机构
Lancaster University Leipzig; Ruhr-University Bochum（莱斯特大学莱比锡分校; 鲁尔-波鸿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在马尔可夫决策过程中，通过扩展dtControl2的“$\varepsilon$”功能，构建更小决策树，在保证$\varepsilon$-最优性的同时提供更简单解释，所建决策树比现有技术小几个数量级。
AI 中文摘要
在过去十年中，决策树已被用于以可解释的方式表示控制器（即策略），dtControl2是当前的先进工具。然而，对于大型或有许多极端情况的系统，即使是这样的表示也往往过于复杂且难以被人类理解。减少决策树的大小并不简单，因为错过一个关键情况可能导致控制器不正确。我们在马尔可夫决策过程的设置中解决这个问题，通过“$\varepsilon$”功能扩展dtControl2：给定允许的不精确性$\varepsilon \geq 0$，我们构建一个更小的决策树，提炼控制器的本质，同时仍保证其$\varepsilon$-最优性。这使我们能够提供可调节的更简单解释，省略可控数量的细节。我们的工具构建的决策树比现有技术小几个数量级。
英文摘要
Over the past decade, decision trees have been used to represent controllers (a.k.a. policies) in an explainable way, with dtControl2 as a current state-of-the-art tool. However, for systems that are large or have many corner cases, even such representations tend to be too complex and not human-comprehensible. Unfortunately, reducing the size of the decision tree is not straightforward, as missing just a single crucial case might result in an incorrect controller. We tackle this issue in the setting of Markov decision processes, extending dtControl2 by "$\varepsilon$" functionality: Given an allowed imprecision $\varepsilon \geq 0$, we construct a smaller decision tree, distilling the essence of the controller, while still guaranteeing its $\varepsilon$-optimality. This enables us to provide tunably simpler explanations, omitting a controllable amount of detail. Our tool constructs decision trees that are orders of magnitude smaller than the state of the art.
CommentsThis paper is accepted at FMCAD26
