We propose and evaluate a new technique for learning hybrid automata automatically by observing the runtime behavior of a dynamical system. Working from a sequence of continuous state values and predicates about the environment, CHARDA recovers the distinct dynamic modes, learns a model for each mode from a given set of templates, and postulates causal guard conditions which trigger transitions between modes. Our main contribution is the use of information-theoretic measures (1)~as a cost function for data segmentation and model selection to penalize over-fitting and (2)~to determine the likely causes of each transition. CHARDA is easily extended with different classes of model templates, fitting methods, or predicates. In our experiments on a complex videogame character, CHARDA successfully discovers a reasonable over-approximation of the character's true behaviors. Our results also compare favorably against recent work in automatically learning probabilistic timed automata in an aircraft domain: CHARDA exactly learns the modes of these simpler automata.

提出并评估了一种新技术，通过观察动态系统的运行时行为自动学习混合自动机。该技术利用信息论度量作为数据分段和模型选择的成本函数，以惩罚过拟合，并确定每个转换的可能原因。通过实验验证，能够成功发现角色的真实行为并与近期自动学习概率定时自动机的工作进行比较，表现出优异的结果。

CHARDA: 通过动态分析实现因果混合自动机恢复