论文标题
代数可解释的控制器：决策树和支持向量机联手
Algebraically Explainable Controllers: Decision Trees and Support Vector Machines Join Forces
论文作者
论文摘要
最近，决策树（DT）已被用作控制器（又称策略，政策，调度程序）的可解释表示。尽管它们通常非常有效，并且为离散系统产生了较小且易于理解的控制器，但复杂的连续动态仍然构成挑战。特别是，当变量之间的关系采用更复杂的形式（例如多项式）时，无法使用可用的DT学习过程获得它们。相比之下，支持向量机提供了更强大的表示，能够发现许多这样的关系，但不能以可解释的形式发现。因此，我们建议将这两个框架结合在一起，以获得对更丰富的，域相关的代数谓词的可理解表示。我们对既定基准进行了实验表明和评估提出的方法。
Recently, decision trees (DT) have been used as an explainable representation of controllers (a.k.a. strategies, policies, schedulers). Although they are often very efficient and produce small and understandable controllers for discrete systems, complex continuous dynamics still pose a challenge. In particular, when the relationships between variables take more complex forms, such as polynomials, they cannot be obtained using the available DT learning procedures. In contrast, support vector machines provide a more powerful representation, capable of discovering many such relationships, but not in an explainable form. Therefore, we suggest to combine the two frameworks in order to obtain an understandable representation over richer, domain-relevant algebraic predicates. We demonstrate and evaluate the proposed method experimentally on established benchmarks.
