{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/charda-causal-hybrid-automata-recovery-via","title":"CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis","arxiv_id":"1707.03336","date":"2017-07-11","proceeding":null,"authors":["Adam Summerville","Joseph Osborn","Michael Mateas"],"abstract":"We propose and evaluate a new technique for learning hybrid automata\nautomatically by observing the runtime behavior of a dynamical system. Working\nfrom a sequence of continuous state values and predicates about the\nenvironment, CHARDA recovers the distinct dynamic modes, learns a model for\neach mode from a given set of templates, and postulates causal guard conditions\nwhich trigger transitions between modes. Our main contribution is the use of\ninformation-theoretic measures (1)~as a cost function for data segmentation and\nmodel selection to penalize over-fitting and (2)~to determine the likely causes\nof each transition. CHARDA is easily extended with different classes of model\ntemplates, fitting methods, or predicates. In our experiments on a complex\nvideogame character, CHARDA successfully discovers a reasonable\nover-approximation of the character's true behaviors. Our results also compare\nfavorably against recent work in automatically learning probabilistic timed\nautomata in an aircraft domain: CHARDA exactly learns the modes of these\nsimpler automata.","url_abs":"http://arxiv.org/abs/1707.03336v1","url_pdf":"http://arxiv.org/pdf/1707.03336v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"charda-causal-hybrid-automata-recovery-via","repo_url":"https://github.com/JoeOsborn/mechlearn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}