{"type":"ScholarlyArticle","title":"An Admissible HTN Planning Heuristic","authors":[{"name":"Pascal Bercher","slug":"pascal-bercher","given":"Pascal","family":"Bercher","affiliation":"Institute of Artificial Intelligence , Ulm University , Ulm , Germany"},{"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":"Hierarchical task network (HTN) planning is well-known for being an efficient planning approach. This is mainly due to the success of the HTN planning system SHOP2. However, its performance depends on hand-designed search control knowledge. At the time being, there are only very few domain-independent heuristics, which are designed for differing hierarchical planning formalisms. Here, we propose an admissible heuristic for standard HTN planning, which allows to find optimal solutions heuristically. It bases upon the so-called task decomposition graph (TDG), a data structure reflecting reachable parts of the task hierarchy. We show (both in theory and empirically) that rebuilding it during planning can improve heuristic accuracy thereby decreasing the explored search space. The evaluation further studies the heuristic both in terms of plan quality and coverage.","doi":"10.24963/ijcai.2017/68","url":"https://ijcai.org/proceedings/2017/68","pdf":"https://ijcai.org/proceedings/2017/0068.pdf","markdown":"https://ijcai.org/proceedings/2017/68.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/68","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":"Combinatorial & Heuristic Search","track":"Main track","number":68,"pages":{"first":480,"last":488},"keywords":["Planning and Scheduling: Search in Planning and Scheduling","Combinatorial & Heuristic Search: Combinatorial search/optimisation","Combinatorial & Heuristic Search: Heuristic Search","Planning and Scheduling: Hierarchical planning"],"videos":[],"references":[{"position":1,"raw":"[Alford et al., 2014] Ron Alford, Vikas Shivashankar, Ugur Kuter, and Dana Nau. 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