{"type":"ScholarlyArticle","title":"Multiple Constraint Acquisition","authors":[{"name":"Robin Arcangioli","slug":"robin-arcangioli","given":"Robin","family":"Arcangioli","affiliation":""},{"name":"Christian Bessière","slug":"christian-bessiere","given":"Christian","family":"Bessiere","affiliation":""},{"name":"Nadjib Lazaar","slug":"nadjib-lazaar","given":"Nadjib","family":"Lazaar","affiliation":""}],"abstract":"QUACQ is a constraint acquisition system that assists a non-expert user to model her problem as a constraint network by classifying (partial) examples as positive or negative. For each negative example, QUACQ focuses onto a constraint of the target network. The drawback is that the user may need to answer a great number of such examples to learn all the constraints. In this paper, we provide a new approach that is able to learn a maximum number of constraints violated by a given negative example. Finally we give an experimental evaluation that shows that our approach improves on QUACQ.","doi":null,"url":"https://ijcai.org/proceedings/2016/105","pdf":"https://ijcai.org/proceedings/2016/0105.pdf","markdown":"https://ijcai.org/proceedings/2016/105.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/105","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":105,"pages":{"first":698,"last":703},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}