{"type":"ScholarlyArticle","title":"Unsatisfiable Core Shrinking for Anytime Answer Set Optimization","authors":[{"name":"Mario Alviano","slug":"mario-alviano","given":"Mario","family":"Alviano","affiliation":"DEMACS , University of Calabria , Italy"},{"name":"Carmine Dodaro","slug":"carmine-dodaro","given":"Carmine","family":"Dodaro","affiliation":"DIBRIS , University of Genova , Italy"}],"abstract":"Efficient algorithms for the computation of optimum stable models are based on unsatisfiable core analysis. However, these algorithms essentially run to completion, providing few or even no suboptimal stable models. This drawback can be circumvented by shrinking unsatisfiable cores. Interestingly, the resulting anytime algorithm can solve more instances than the original algorithm.","doi":"10.24963/ijcai.2017/666","url":"https://ijcai.org/proceedings/2017/666","pdf":"https://ijcai.org/proceedings/2017/0666.pdf","markdown":"https://ijcai.org/proceedings/2017/666.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/666","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Best Sister Conferences","track":"Best Sister Conferences","number":666,"pages":{"first":4781,"last":4785},"keywords":["Artificial Intelligence: knowledge representation and reasoning","Artificial Intelligence: artificial intelligence"],"videos":[],"references":[{"position":1,"raw":"[Alviano and Dodaro, 2016a] Mario Alviano and Carmine Dodaro. 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