Recent advances have shown how decision trees are apt data structures for concisely representing strategies (or controllers) satisfying various objectives. Moreover, they also make the strategy more explainable. The recent tool dtControl had provided pipelines with tools supporting strategy synthesis for hybrid systems, such as SCOTS and Uppaal Stratego. We present dtControl 2.0, a new version with several fundamentally novel features. Most importantly, the user can now provide domain knowledge to be exploited in the decision tree learning process and can also interactively steer the process based on the dynamically provided information. To this end, we also provide a graphical user interface. It allows for inspection and re-computation of parts of the result, suggesting as well as receiving advice on predicates, and visual simulation of the decision-making process. Besides, we interface model checkers of probabilistic systems, namely Storm and PRISM and provide dedicated support for categorical enumeration-type state variables. Consequently, the controllers are more explainable and smaller.

中文翻译：

---

dtControl 2.0：通过专家指导的决策树学习进行可解释的策略表示

最近的进展表明，决策树如何适合用于简洁地表示满足各种目标的策略（或控制器）的数据结构。此外，它们还使该策略更具可解释性。最近的工具dtControl为管道提供了支持混合系统（如SCOTS和Uppaal Stratego）的策略综合的工具。我们介绍了dtControl 2.0，它是一个具有一些根本上新颖功能的新版本。最重要的是，用户现在可以提供在决策树学习过程中要利用的领域知识，并且还可以基于动态提供的信息以交互方式指导过程。为此，我们还提供了图形用户界面。它允许对结果的一部分进行检查和重新计算，并就谓词提出建议并获得建议，以及决策过程的可视化模拟。此外，我们连接了概率系统（即Storm和PRISM）的模型检查器，并为分类枚举类型的状态变量提供了专门的支持。因此，控制器更易于解释且更小。