dtControl：用于控制器表示的决策树学习算法
dtControl: Decision Tree Learning Algorithms for Controller Representation
摘要：
决策树学习是机器学习应用中最常用的流行分类技术。最近的工作表明，决策树可以用来表示证明正确的控制器简洁。与使用查找表或二元决策图的表示相比，决策树更小，更易于解释。我们提出dtControl，一个易于扩展的工具，用于表示无记忆控制器作为决策树。我们给出了一个综合评价的各种决策树学习算法适用于10个案例研究所产生的正确的建设控制器合成。这些算法包括两个新的技术，一个是使用任意线性二进制分类器的决策树学习，和一个新的方法来确定控制器在决策树建设。特别是后者被证明是非常有效的，产生决策树与一位数的决策节点的5个案例研究。
英文摘要：
Decision tree learning is a popular classification technique most commonly used in machine learning applications. Recent work has shown that decision trees can be used to represent provably-correct controllers concisely. Compared to representations using lookup tables or binary decision diagrams, decision trees are smaller and more explainable. We present dtControl, an easily extensible tool for representing memoryless controllers as decision trees. We give a comprehensive evaluation of various decision tree learning algorithms applied to 10 case studies arising out of correct-by-construction controller synthesis. These algorithms include two new techniques, one for using arbitrary linear binary classifiers in the decision tree learning, and one novel approach for determinizing controllers during the decision tree construction. In particular the latter turns out to be extremely efficient, yielding decision trees with a single-digit number of decision nodes on 5 of the case studies.
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