{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T10:12:38Z","timestamp":1767262358459},"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":[[2021,8]]},"abstract":"<jats:p>Bayesian networks are probabilistic graphical models with a wide\n\nrange of application areas including gene regulatory networks inference, risk\n\nanalysis and image processing. Learning the structure of a Bayesian\n\nnetwork (BNSL) from discrete data is known to be an NP-hard task with\n\na superexponential search space of directed acyclic graphs.  In this\n\nwork, we propose a new polynomial time algorithm for discovering a\n\nsubset of all possible cluster cuts, a greedy algorithm for\n\napproximately solving the resulting linear program, and a generalized\n\narc consistency algorithm for the acyclicity constraint.  We embed\n\nthese in the constraint programming-based branch-and-bound solver\n\nCPBayes and show that, despite being suboptimal, they improve\n\nperformance by orders of magnitude. The resulting solver also compares\n\nfavorably with GOBNILP, a state-of-the-art solver for the BNSL\n\nproblem which solves an NP-hard problem to discover each cut and\n\nsolves the linear program exactly.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/584","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"4250-4257","source":"Crossref","is-referenced-by-count":4,"title":["Improved Acyclicity Reasoning for Bayesian Network Structure Learning with Constraint Programming"],"prefix":"10.24963","author":[{"given":"Fulya","family":"Tr\u00f6sser","sequence":"first","affiliation":[{"name":"Universit\u00e9 de Toulouse, INRAE, UR MIAT, F-31320, Castanet-Tolosan, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"de Givry","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Toulouse, INRAE, UR MIAT, F-31320, Castanet-Tolosan, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Katsirelos","sequence":"additional","affiliation":[{"name":"UMR MIA-Paris, INRAE, AgroParisTech, Univ. Paris-Saclay, 75005 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2021","name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","start":{"date-parts":[[2021,8,19]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:04:10Z","timestamp":1628679850000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/584"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/584","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}