{"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/online-learning-of-event-definitions","title":"Online Learning of Event Definitions","arxiv_id":"1608.00100","date":"2016-07-30","proceeding":null,"authors":["Nikos Katzouris","Alexander Artikis","Georgios Paliouras"],"abstract":"Systems for symbolic event recognition infer occurrences of events in time\nusing a set of event definitions in the form of first-order rules. The Event\nCalculus is a temporal logic that has been used as a basis in event recognition\napplications, providing among others, direct connections to machine learning,\nvia Inductive Logic Programming (ILP). We present an ILP system for online\nlearning of Event Calculus theories. To allow for a single-pass learning\nstrategy, we use the Hoeffding bound for evaluating clauses on a subset of the\ninput stream. We employ a decoupling scheme of the Event Calculus axioms during\nthe learning process, that allows to learn each clause in isolation. Moreover,\nwe use abductive-inductive logic programming techniques to handle unobserved\ntarget predicates. We evaluate our approach on an activity recognition\napplication and compare it to a number of batch learning techniques. We obtain\nresults of comparable predicative accuracy with significant speed-ups in\ntraining time. We also outperform hand-crafted rules and match the performance\nof a sound incremental learner that can only operate on noise-free datasets.\nThis paper is under consideration for acceptance in TPLP.","url_abs":"http://arxiv.org/abs/1608.00100v1","url_pdf":"http://arxiv.org/pdf/1608.00100v1.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":"online-learning-of-event-definitions","repo_url":"https://github.com/nkatzz/OLED","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"inductive-logic-programming","task_name":"Inductive logic programming"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}