dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts

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 had provided pipelines with tools supporting strategy synthesis for hybrid systems, such as and . We present , 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 and and provide dedicated support for categorical enumeration-type state variables. Consequently, the controllers are more explainable and smaller.