{"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/probabilistic-event-calculus-for-event","title":"Probabilistic Event Calculus for Event Recognition","arxiv_id":"1207.3270","date":"2012-07-13","proceeding":null,"authors":["Anastasios Skarlatidis","Georgios Paliouras","Alexander Artikis","George A. Vouros"],"abstract":"Symbolic event recognition systems have been successfully applied to a\nvariety of application domains, extracting useful information in the form of\nevents, allowing experts or other systems to monitor and respond when\nsignificant events are recognised. In a typical event recognition application,\nhowever, these systems often have to deal with a significant amount of\nuncertainty. In this paper, we address the issue of uncertainty in logic-based\nevent recognition by extending the Event Calculus with probabilistic reasoning.\nMarkov Logic Networks are a natural candidate for our logic-based formalism.\nHowever, the temporal semantics of the Event Calculus introduce a number of\nchallenges for the proposed model. We show how and under what assumptions we\ncan overcome these problems. Additionally, we study how probabilistic modelling\nchanges the behaviour of the formalism, affecting its key property, the inertia\nof fluents. Furthermore, we demonstrate the advantages of the probabilistic\nEvent Calculus through examples and experiments in the domain of activity\nrecognition, using a publicly available dataset for video surveillance.","url_abs":"http://arxiv.org/abs/1207.3270v2","url_pdf":"http://arxiv.org/pdf/1207.3270v2.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":"probabilistic-event-calculus-for-event","repo_url":"https://github.com/koo5/notes2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}