{"type":"ScholarlyArticle","title":"Approximate Inference in Logical Credal Networks","authors":[{"name":"Radu Marinescu","slug":"radu-marinescu","given":"Radu","family":"Marinescu","affiliation":"IBM Research"},{"name":"Haifeng Qian","slug":"haifeng-qian","given":"Haifeng","family":"Qian","affiliation":"AWS AI Labs"},{"name":"Alexander Gray","slug":"alexander-gray","given":"Alexander","family":"Gray","affiliation":"IBM Research"},{"name":"Debarun Bhattacharjya","slug":"debarun-bhattacharjya","given":"Debarun","family":"Bhattacharjya","affiliation":"IBM Research"},{"name":"Francisco Barahona","slug":"francisco-barahona","given":"Francisco","family":"Barahona","affiliation":"IBM Research"},{"name":"Tian Gao","slug":"tian-gao","given":"Tian","family":"Gao","affiliation":"IBM Research"},{"name":"Ryan Riegel","slug":"ryan-riegel","given":"Ryan","family":"Riegel","affiliation":"IBM Research"}],"abstract":"The Logical Credal Network or LCN is a recent probabilistic logic designed for effective aggregation and reasoning over multiple sources of imprecise knowledge. An LCN specifies a set of probability distributions over all interpretations of a set of logical formulas for which marginal and conditional probability bounds on their truth values are known. Inference in LCNs involves the exact solution of a non-convex non-linear program defined over an exponentially large number of non-negative real valued variables and, therefore, is limited to relatively small problems. In this paper, we present ARIEL -- a novel iterative message-passing scheme for approximate inference in LCNs. Inspired by classical belief propagation for graphical models, our method propagates messages that involve solving considerably smaller local non-linear programs. Experiments on several classes of LCNs demonstrate clearly that ARIEL yields high quality solutions compared with exact inference and scales to much larger problems than previously considered.","doi":"10.24963/ijcai.2023/632","url":"https://ijcai.org/proceedings/2023/632","pdf":"https://ijcai.org/proceedings/2023/0632.pdf","markdown":"https://ijcai.org/proceedings/2023/632.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/632","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Uncertainty in AI","track":"Main Track","number":632,"pages":{"first":5694,"last":5701},"keywords":["Knowledge Representation and Reasoning: KRR: Knowledge representation languages","Uncertainty in AI: UAI: Graphical models","Uncertainty in AI: UAI: Inference"],"videos":[],"references":[{"position":1,"raw":"[Andersen and Hooker, 1994] Kent Andersen and John Hooker. 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