{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:30:25Z","timestamp":1723015825542},"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":[[2017,8]]},"abstract":"<jats:p>We propose a new abstraction refinement procedure based on machine learning to improve the performance of nonlinear constraint solving algorithms on large-scale problems. The proposed approach decomposes the original set of constraints into smaller subsets, and uses learning algorithms to propose sequences of abstractions that take the form of conjunctions of classifiers. The core procedure is a refinement loop that keeps improving the learned results based on counterexamples that are obtained from partial constraints that are easy to solve. Experiments show that the proposed techniques significantly improve the performance of state-of-the-art constraint solvers on many challenging benchmarks. The mechanism is capable of producing intermediate symbolic abstractions that are also important for many applications and for understanding the internal structures of hard constraint solving problems.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/83","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"592-599","source":"Crossref","is-referenced-by-count":2,"title":["Learning-Based Abstractions for Nonlinear Constraint Solving"],"prefix":"10.24963","author":[{"given":"Sumanth","family":"Dathathri","sequence":"first","affiliation":[{"name":"California Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikos","family":"Arechiga","sequence":"additional","affiliation":[{"name":"Toyota ITC"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sicun","family":"Gao","sequence":"additional","affiliation":[{"name":"University of California San Diego"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard M.","family":"Murray","sequence":"additional","affiliation":[{"name":"California Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T07:52:10Z","timestamp":1501228330000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/83"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/83","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}