{"type":"ScholarlyArticle","title":"Sampling-Based Belief Revision","authors":[{"name":"Michael Thielscher","slug":"michael-thielscher","given":"Michael","family":"Thielscher","affiliation":""}],"abstract":"Model sampling has proved to be a practically viable method for decision-making under uncertainty, for example in imperfect-information games with large state spaces. In this paper, we examine the logical foundations of sampling-based belief revision. We show that it satisfies six of the standard AGM postulates but not Vacuity nor Subexpansion. We provide a corresponding representation theorem that generalises the standard result from a single to a family of faithful assignments for a given belief set. We also provide a formal axiomatisation of sampling-based belief revision in the Situation Calculus as an alternative way of reasoning about actions, sensing, and beliefs.","doi":null,"url":"https://ijcai.org/proceedings/2016/184","pdf":"https://ijcai.org/proceedings/2016/0184.pdf","markdown":"https://ijcai.org/proceedings/2016/184.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/184","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":184,"pages":{"first":1272,"last":1277},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}