{"type":"ScholarlyArticle","title":"Learning Constraint Networks over Unknown Constraint Languages","authors":[{"name":"Christian Bessiere","slug":"christian-bessiere","given":"Christian","family":"Bessiere","affiliation":"University of Montpellier , CNRS , LIRMM , Montpellier , France"},{"name":"Clément Carbonnel","slug":"clement-carbonnel","given":"Clément","family":"Carbonnel","affiliation":"University of Montpellier , CNRS , LIRMM , Montpellier , France"},{"name":"Areski Himeur","slug":"areski-himeur","given":"Areski","family":"Himeur","affiliation":"University of Montpellier , CNRS , LIRMM , Montpellier , France"}],"abstract":"Constraint acquisition is the task of learning a constraint network from examples of solutions and non-solutions. Existing constraint acquisition systems typically require advance knowledge of the target network's constraint language, which significantly narrows their scope of applicability. In this paper we propose a constraint acquisition method that computes a suitable constraint language as part of the learning process, eliminating the need for any advance knowledge. We report preliminary experiments on various acquisition benchmarks.","doi":"10.24963/ijcai.2023/208","url":"https://ijcai.org/proceedings/2023/208","pdf":"https://ijcai.org/proceedings/2023/0208.pdf","markdown":"https://ijcai.org/proceedings/2023/208.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/208","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Constraint Satisfaction and Optimization","track":"Main Track","number":208,"pages":{"first":1876,"last":1883},"keywords":["Constraint Satisfaction and Optimization: CSO: Constraint programming","Constraint Satisfaction and Optimization: CSO: Constraint learning and acquisition"],"videos":[],"references":[{"position":1,"raw":"[Beldiceanu and Simonis, 2012] Nicolas Beldiceanu and Helmut Simonis. A model seeker: Extracting global constraint models from positive examples. In Michela Milano, editor, Principles and Practice of Constraint Programming -18th International Conference, CP 2012, Québec City, QC, Canada, October 8-12, 2012. Proceed- ings, volume 7514 of Lecture Notes in Computer Science, pages 141-157. Springer, 2012.","title":"A model seeker: Extracting global constraint models from positive examples","authors":"Simonis Beldiceanu, Nicolas Beldiceanu, Helmut Simonis","year":"2012","venue":"Principles and Practice of Constraint Programming -18th International Conference, CP 2012","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bessiere et al., 2005] Christian Bessiere, Remi Coletta, Frédéric Koriche, and Barry O'Sullivan. A sat-based ver- sion space algorithm for acquiring constraint satisfaction problems. In João Gama, Rui Camacho, Pavel Brazdil, Alípio Jorge, and Luís Torgo, editors, Machine Learn- ing: ECML 2005, 16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, Proceed- ings, volume 3720 of Lecture Notes in Computer Science, pages 23-34. Springer, 2005.","title":"A sat-based version space algorithm for acquiring constraint satisfaction problems","authors":"Bessiere","year":"2005","venue":"Machine Learning: ECML 2005, 16th European Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bessiere et al., 2009] Christian Bessiere, Emmanuel He- brard, Brahim Hnich, Zeynep Kiziltan, and Toby Walsh. Range and roots: Two common patterns for specifying and propagating counting and occurrence constraints. Artif. In- tell., 173(11):1054-1078, 2009.","title":"Range and roots: Two common patterns for specifying and propagating counting and occurrence constraints","authors":"Bessiere","year":"2009","venue":"Artif. Intell","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bessiere et al., 2017] Christian Bessiere, Frédéric Koriche, Nadjib Lazaar, and Barry O'Sullivan. Constraint acquisi- tion. Artif. Intell., 244:315-342, 2017.","title":"Christian Bessiere, Frédéric Koriche, Nadjib Lazaar, and Barry O'Sullivan. Constraint acquisition","authors":"Bessiere","year":"2017","venue":"Artif. Intell","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Kumar et al., 2019] Mohit Kumar, Stefano Teso, and Luc De Raedt. Acquiring integer programs from data. In Sarit Kraus, editor, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pages 1130-1136. ijcai.org, 2019.","title":"Acquiring integer programs from data","authors":"Mohit Kumar, Stefano Teso, Luc De, Raedt","year":"2019","venue":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019","doi":null,"url":null,"internal_url":"/proceedings/2019/158"},{"position":6,"raw":"[Kumar et al., 2022] Mohit Kumar, Samuel Kolb, and Tias Guns. Learning constraint programming models from data using generate-and-aggregate. In Christine Solnon, editor, 28th International Conference on Principles and Practice of Constraint Programming, CP 2022, July 31 to August 8, 2022, Haifa, Israel, volume 235 of LIPIcs, pages 29:1- 29:16. Schloss Dagstuhl -Leibniz-Zentrum für Informatik, 2022.","title":"Learning constraint programming models from data using generate-and-aggregate","authors":"Mohit Kumar, Samuel Kolb, Tias Guns","year":"2022","venue":"28th International Conference on Principles and Practice of Constraint Programming","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Pawlak and Krawiec, 2017] Tomasz P. Pawlak and Krzysztof Krawiec. Automatic synthesis of constraints from examples using mixed integer linear programming. Eur. J. Oper. Res., 261(3):1141-1157, 2017.","title":"Automatic synthesis of constraints from examples using mixed integer linear programming","authors":"P Tomasz, Krzysztof Pawlak, Krawiec","year":"2017","venue":"Eur. J. Oper. Res","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Piotrów, 2020] Marek Piotrów. Uwrmaxsat: Efficient solver for maxsat and pseudo-boolean problems. In 32nd IEEE International Conference on Tools with Artificial Intelli- gence, ICTAI 2020, Baltimore, MD, USA, November 9-11, 2020, pages 132-136. IEEE, 2020.","title":"Marek Piotrów. Uwrmaxsat: Efficient solver for maxsat and pseudo-boolean problems","authors":"Piotrów","year":"2020","venue":"32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Prestwich et al., 2021] Steven D. Prestwich, Eugene C. Freuder, Barry O'Sullivan, and David Browne. Classifier- based constraint acquisition. Ann. Math. Artif. Intell., 89(7):655-674, 2021.","title":"Classifierbased constraint acquisition","authors":"Steven D Prestwich, Eugene C Freuder, O' Barry, David Sullivan, Browne","year":"2021","venue":"Ann. Math. Artif. Intell","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}