{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T17:01:26Z","timestamp":1762102886424},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>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. \u00a0However, existing WMI solvers each have different interfaces and use different formats for representing WMI problems. \u00a0Therefore, we introduce pywmi  (http:\/\/pywmi.org), an open source framework and toolbox for probabilistic inference using WMI, to address these shortcomings. \u00a0Crucially, pywmi fixes a common internal format for WMI problems and introduces a common interface for WMI solvers. \u00a0To assist users in modeling WMI problems, pywmi introduces modeling languages based on SMT-LIB.v2 or MiniZinc and parsers for both. \u00a0To 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. \u00a0Finally, to assist developers in implementing new solvers, pywmi provides Python implementations of commonly used subroutines.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/946","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"6530-6532","source":"Crossref","is-referenced-by-count":0,"title":["The pywmi Framework and Toolbox for Probabilistic Inference using Weighted Model Integration"],"prefix":"10.24963","author":[{"given":"Samuel","family":"Kolb","sequence":"first","affiliation":[{"name":"KU Leuven"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paolo","family":"Morettin","sequence":"additional","affiliation":[{"name":"University of Trento"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro","family":"Zuidberg Dos Martires","sequence":"additional","affiliation":[{"name":"KU Leuven"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco","family":"Sommavilla","sequence":"additional","affiliation":[{"name":"University of Trento"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Passerini","sequence":"additional","affiliation":[{"name":"University of Trento"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Sebastiani","sequence":"additional","affiliation":[{"name":"University of Trento"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luc","family":"De Raedt","sequence":"additional","affiliation":[{"name":"KU Leuven"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:52:57Z","timestamp":1564285977000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/946"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/946","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}