{"type":"ScholarlyArticle","title":"Stochastic Constraint Propagation for Mining Probabilistic Networks","authors":[{"name":"Anna Louise D. Latour","slug":"anna-louise-d-latour","given":"Anna Louise D.","family":"Latour","affiliation":"Leiden University , Leiden , The Netherlands"},{"name":"Behrouz Babaki","slug":"behrouz-babaki","given":"Behrouz","family":"Babaki","affiliation":"Polytechnique Montréal , Montreal , Canada"},{"name":"Siegfried Nijssen","slug":"siegfried-nijssen","given":"Siegfried","family":"Nijssen","affiliation":"UCLouvain , Louvain-la-Neuve , Belgium"}],"abstract":"A number of data mining problems on probabilistic networks can be modeled as Stochastic Constraint Optimization and Satisfaction Problems, i.e., problems that involve objectives or constraints with a stochastic component. Earlier methods for solving these problems used Ordered Binary Decision Diagrams (OBDDs) to represent constraints on probability distributions, which were decomposed into sets of smaller constraints and solved by Constraint Programming (CP) or Mixed Integer Programming (MIP) solvers. For the specific case of monotonic distributions, we propose an alternative method: a new propagator for a global OBDD-based constraint. We show that this propagator is (sub-)linear in the size of the OBDD, and maintains domain consistency. We experimentally evaluate the effectiveness of this global constraint in comparison to existing decomposition-based approaches, and show how this propagator can be used in combination with another data mining specific constraint present in CP systems. As test cases we use problems from the data mining literature.","doi":"10.24963/ijcai.2019/159","url":"https://ijcai.org/proceedings/2019/159","pdf":"https://ijcai.org/proceedings/2019/0159.pdf","markdown":"https://ijcai.org/proceedings/2019/159.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/159","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Constraints and SAT","track":"Main track","number":159,"pages":{"first":1137,"last":1145},"keywords":["Constraints and SAT: Global Constraints","Uncertainty in AI: Uncertainty in AI","Heuristic Search and Game Playing: Combinatorial Search and Optimisation","Uncertainty in AI: Exact Probabilistic Inference"],"videos":[],"references":[{"position":1,"raw":"[Bryant, 1986] Randal E. 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