{"type":"ScholarlyArticle","title":"Explaining Soft-Goal Conflicts through Constraint Relaxations","authors":[{"name":"Rebecca Eifler","slug":"rebecca-eifler","given":"Rebecca","family":"Eifler","affiliation":"Saarland University , Saarland Informatics Campus , Germany"},{"name":"Jeremy Frank","slug":"jeremy-frank","given":"Jeremy","family":"Frank","affiliation":"NASA Ames Research Center , Mountain View , CA , USA"},{"name":"Jörg Hoffmann","slug":"jorg-hoffmann","given":"Jörg","family":"Hoffmann","affiliation":"Saarland University , Saarland Informatics Campus , Germany; German Research Center for Artificial Intelligence (DFKI) , Saarbrücken , Germany"}],"abstract":"Recent work suggests to explain trade-offs between soft-goals in terms of their conflicts, i.e., minimal unsolvable soft-goal subsets. But this does not explain the conflicts themselves: Why can a given set of soft-goals not be jointly achieved? Here we approach that question in terms of the underlying constraints on plans in the task at hand, namely resource availability and time windows. In this context, a natural form of explanation for a soft-goal conflict is a minimal constraint relaxation under which the conflict disappears (``if the deadline was 1 hour later, it would work''). We explore algorithms for computing such explanations. A baseline is to simply loop over all relaxed tasks and compute the conflicts for each separately. We improve over this by two algorithms that leverage information -- conflicts, reachable states -- across relaxed tasks. We show that these algorithms can exponentially outperform the baseline in theory, and we run experiments confirming that advantage in practice.","doi":"10.24963/ijcai.2022/641","url":"https://ijcai.org/proceedings/2022/641","pdf":"https://ijcai.org/proceedings/2022/0641.pdf","markdown":"https://ijcai.org/proceedings/2022/641.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/641","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Planning and Scheduling","track":"Main Track","number":641,"pages":{"first":4621,"last":4627},"keywords":["Planning and Scheduling: Planning Algorithms"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986707","length_sec":87,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984850","length_sec":357,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Agrawal et al., 2020] Jagriti Agrawal, Amruta Yelaman- chili, and Steve Chien. 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