CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis

CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis

Adam Summerville, Joseph Osborn, Michael Mateas

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 2800-2806. https://doi.org/10.24963/ijcai.2017/390

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 \textit{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 selction 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.
Keywords:
Machine Learning: Machine Learning
Machine Learning: Time-series/Data Streams
Multidisciplinary Topics and Applications: Computer Games