{"type":"ScholarlyArticle","title":"Finding Optimal Solutions in HTN Planning - A SAT-based Approach","authors":[{"name":"Gregor Behnke","slug":"gregor-behnke","given":"Gregor","family":"Behnke","affiliation":"Institute of Artificial Intelligence , Ulm University , Ulm , Germany"},{"name":"Daniel Höller","slug":"daniel-holler","given":"Daniel","family":"Höller","affiliation":"Institute of Artificial Intelligence , Ulm University , Ulm , Germany"},{"name":"Susanne Biundo","slug":"susanne-biundo","given":"Susanne","family":"Biundo","affiliation":"Institute of Artificial Intelligence , Ulm University , Ulm , Germany"}],"abstract":"Over the last years, several new approaches to Hierarchical Task Network (HTN) planning have been proposed that increased the overall performance of HTN planners.\r\nHowever, the focus has been on agile planning - on finding a solution as quickly as possible.\r\nLittle work has been done on finding optimal plans.\r\nWe show how the currently best-performing approach to HTN planning - the translation into propositional logic - can be utilised to find optimal plans.\r\nSuch SAT-based planners usually bound the HTN problem to a certain depth of decomposition and then translate the problem into a propositional formula.\r\nTo generate optimal plans, the length of the solution has to be bounded instead of the decomposition depth.\r\nWe show the relationship between these bounds and how it can be handled algorithmically.\r\nBased on this, we propose an optimal SAT-based HTN planner and show that it performs favourably on a benchmark set.","doi":"10.24963/ijcai.2019/764","url":"https://ijcai.org/proceedings/2019/764","pdf":"https://ijcai.org/proceedings/2019/0764.pdf","markdown":"https://ijcai.org/proceedings/2019/764.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/764","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Planning and Scheduling","track":"Main track","number":764,"pages":{"first":5500,"last":5508},"keywords":["Planning and Scheduling: Planning Algorithms","Planning and Scheduling: Hierarchical planning","Constraints and SAT: SAT: Applications"],"videos":[],"references":[{"position":1,"raw":"[Alford et al., 2016] Ron Alford, Gregor Behnke, Daniel Höller, Pascal Bercher, Susanne Biundo, and David W. 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