{"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-inductive-logic-programming","title":"Probabilistic Inductive Logic Programming Based on Answer Set Programming","arxiv_id":"1405.0720","date":"2014-05-04","proceeding":null,"authors":["Matthias Nickles","Alessandra Mileo"],"abstract":"We propose a new formal language for the expressive representation of\nprobabilistic knowledge based on Answer Set Programming (ASP). It allows for\nthe annotation of first-order formulas as well as ASP rules and facts with\nprobabilities and for learning of such weights from data (parameter\nestimation). Weighted formulas are given a semantics in terms of soft and hard\nconstraints which determine a probability distribution over answer sets. In\ncontrast to related approaches, we approach inference by optionally utilizing\nso-called streamlining XOR constraints, in order to reduce the number of\ncomputed answer sets. Our approach is prototypically implemented. Examples\nillustrate the introduced concepts and point at issues and topics for future\nresearch.","url_abs":"http://arxiv.org/abs/1405.0720v1","url_pdf":"http://arxiv.org/pdf/1405.0720v1.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-inductive-logic-programming","repo_url":"https://github.com/MatthiasNickles/diff-SAT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"inductive-logic-programming","task_name":"Inductive logic programming"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}