{"type":"ScholarlyArticle","title":"CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis","authors":[{"name":"Adam Summerville","slug":"adam-summerville","given":"Adam","family":"Summerville","affiliation":"University of California , Santa Cruz"},{"name":"Joseph Osborn","slug":"joseph-osborn","given":"Joseph","family":"Osborn","affiliation":"University of California , Santa Cruz"},{"name":"Michael Mateas","slug":"michael-mateas","given":"Michael","family":"Mateas","affiliation":"University of California , Santa Cruz"}],"abstract":"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.","doi":"10.24963/ijcai.2017/390","url":"https://ijcai.org/proceedings/2017/390","pdf":"https://ijcai.org/proceedings/2017/0390.pdf","markdown":"https://ijcai.org/proceedings/2017/390.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/390","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":390,"pages":{"first":2800,"last":2806},"keywords":["Machine Learning: Machine Learning","Multidisciplinary Topics and Applications: Computer Games","Machine Learning: Time-series/Data Streams"],"videos":[],"references":[{"position":1,"raw":"[Alur et al., 1993] Rajeev Alur, Costas Courcoubetis, Thomas A Henzinger, and Pei-Hsin Ho. 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