{"type":"ScholarlyArticle","title":"The pywmi Framework and Toolbox for Probabilistic Inference using Weighted Model Integration","authors":[{"name":"Samuel Kolb","slug":"samuel-kolb","given":"Samuel","family":"Kolb","affiliation":"KU Leuven"},{"name":"Paolo Morettin","slug":"paolo-morettin","given":"Paolo","family":"Morettin","affiliation":"University of Trento"},{"name":"Pedro Zuidberg Dos Martires","slug":"pedro-zuidberg-dos-martires","given":"Pedro","family":"Zuidberg Dos Martires","affiliation":"KU Leuven"},{"name":"Francesco Sommavilla","slug":"francesco-sommavilla","given":"Francesco","family":"Sommavilla","affiliation":"University of Trento"},{"name":"Andrea Passerini","slug":"andrea-passerini","given":"Andrea","family":"Passerini","affiliation":"University of Trento"},{"name":"Roberto Sebastiani","slug":"roberto-sebastiani","given":"Roberto","family":"Sebastiani","affiliation":"University of Trento"},{"name":"Luc De Raedt","slug":"luc-de-raedt","given":"Luc","family":"de Raedt","affiliation":"KU Leuven"}],"abstract":"Weighted Model Integration (WMI) is a popular technique for probabilistic inference that extends Weighted Model Counting (WMC) -- the standard inference technique for inference in discrete domains -- to domains with both discrete and continuous variables.  However, existing WMI solvers each have different interfaces and use different formats for representing WMI problems.  Therefore, we introduce pywmi  (http://pywmi.org), an open source framework and toolbox for probabilistic inference using WMI, to address these shortcomings.  Crucially, pywmi fixes a common internal format for WMI problems and introduces a common interface for WMI solvers.  To assist users in modeling WMI problems, pywmi introduces modeling languages based on SMT-LIB.v2 or MiniZinc and parsers for both.  To assist users in comparing WMI solvers, pywmi includes implementations of several state-of-the-art solvers, a fast approximate WMI solver, and a command-line interface to solve WMI problems.  Finally, to assist developers in implementing new solvers, pywmi provides Python implementations of commonly used subroutines.","doi":"10.24963/ijcai.2019/946","url":"https://ijcai.org/proceedings/2019/946","pdf":"https://ijcai.org/proceedings/2019/0946.pdf","markdown":"https://ijcai.org/proceedings/2019/946.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/946","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":"Demos","track":"Demos","number":946,"pages":{"first":6530,"last":6532},"keywords":["AI: Knowledge Representation and Reasoning","AI: Uncertainty in AI"],"videos":[],"references":[{"position":1,"raw":"[Barrett et al., 2010] Clark Barrett, Aaron Stump, and Ce- sare Tinelli. 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