{"type":"ScholarlyArticle","title":"A Unifying Framework for Probabilistic Belief Revision","authors":[{"name":"Zhiqiang Zhuang","slug":"zhiqiang-zhuang","given":"Zhiqiang","family":"Zhuang","affiliation":"Griffith University"},{"name":"James Delgrande","slug":"james-delgrande","given":"James","family":"Delgrande","affiliation":"University"},{"name":"Abhaya Nayak","slug":"abhaya-nayak","given":"Abhaya","family":"Nayak","affiliation":"Macquarie University"},{"name":"Abdul Sattar","slug":"abdul-sattar","given":"Abdul","family":"Sattar","affiliation":"Griffith University"}],"abstract":"In this paper we provide a general, unifying framework for probabilistic belief revision. We first introduce a probabilistic logic called p-logic  that is capable of representing and reasoning with basic probabilistic information. With p-logic as the background logic, we define a revision function called p-revision that resembles  partial meet revision in the AGM framework. We provide a representation theorem for p-revision which shows that it can be characterised by the set of  basic AGM revision postulates. P-revision represents an \"all purpose\" method for revising  probabilistic information that  can be used for, but not limited to, the revision problems behind  Bayesian conditionalisation,  Jeffrey conditionalisation, and Lewis's imaging. Importantly, p-revision subsumes all three approaches indicating that Bayesian conditionalisation,  Jeffrey conditionalisation, and Lewis' imaging  all obey the basic principles of AGM revision. As well our investigation sheds light on the corresponding operation of AGM expansion in the probabilistic setting.","doi":"10.24963/ijcai.2017/190","url":"https://ijcai.org/proceedings/2017/190","pdf":"https://ijcai.org/proceedings/2017/0190.pdf","markdown":"https://ijcai.org/proceedings/2017/190.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/190","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Knowledge Representation, Reasoning, and Logic","track":"Main track","number":190,"pages":{"first":1370,"last":1376},"keywords":["Knowledge Representation, Reasoning, and Logic: Non-monotonic Reasoning","Knowledge Representation, Reasoning, and Logic: Logics for Knowledge Representation","Knowledge Representation, Reasoning, and Logic: Belief Change"],"videos":[],"references":[{"position":1,"raw":"[Alchourrón et al., 1985] Carlos E. 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