{"type":"ScholarlyArticle","title":"Epistemic Logic of Likelihood and Belief","authors":[{"name":"James P. Delgrande","slug":"james-p-delgrande","given":"James P.","family":"Delgrande","affiliation":"School of Computing Science , Simon Fraser University , Burnaby , B.C. , V5A 1S6 Canada."},{"name":"Joshua Sack","slug":"joshua-sack","given":"Joshua","family":"Sack","affiliation":"Dept. of Math. and Statistics , California State University Long Beach , CA 90840 , USA."},{"name":"Gerhard Lakemeyer","slug":"gerhard-lakemeyer","given":"Gerhard","family":"Lakemeyer","affiliation":"Dept. of Computer Science , RWTH Aachen University , D-52056 Aachen , Germany."},{"name":"Maurice Pagnucco","slug":"maurice-pagnucco","given":"Maurice","family":"Pagnucco","affiliation":"School of Computer Science and Engineering , UNSW , Sydney , NSW 2052 , Australia."}],"abstract":"A major challenge in AI is dealing with uncertain information. While probabilistic approaches have been employed to address this issue, in many situations probabilities may not be available or may be unsuitable. As an alternative, qualitative approaches have been introduced to express that one event is no more probable than another. We provide an approach where an agent may reason deductively about notions of likelihood, and may hold beliefs where the subjective probability for a belief is less than 1. Thus, an agent can believe that p holds (with probability <1); and if the agent believes that q is more likely than p, then the agent will also believe q. Our language allows for arbitrary nesting of beliefs and qualitative likelihoods. We provide a sound and complete proof system for the logic with respect to an underlying probabilistic semantics, and show that the language is equivalent to a sublanguage with no nested modalities.","doi":"10.24963/ijcai.2022/360","url":"https://ijcai.org/proceedings/2022/360","pdf":"https://ijcai.org/proceedings/2022/0360.pdf","markdown":"https://ijcai.org/proceedings/2022/360.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/360","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":360,"pages":{"first":2599,"last":2605},"keywords":["Knowledge Representation and Reasoning: Reasoning about Knowledge and Belief","Agent-based and Multi-agent Systems: Agent Theories and Models","Knowledge Representation and Reasoning: Knowledge Representation Languages"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986899","length_sec":80,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985042","length_sec":351,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Belle, 2017] Vaishak Belle. 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