{"type":"ScholarlyArticle","title":"State-Dependent Cost Partitionings for Cartesian Abstractions in Classical Planning","authors":[{"name":"Thomas Keller","slug":"thomas-keller","given":"Thomas","family":"Keller","affiliation":""},{"name":"Florian Pommerening","slug":"florian-pommerening","given":"Florian","family":"Pommerening","affiliation":""},{"name":"Jendrik Seipp","slug":"jendrik-seipp","given":"Jendrik","family":"Seipp","affiliation":""},{"name":"Florian Geißer","slug":"florian-gei-er","given":"Florian","family":"Geißer","affiliation":""},{"name":"Robert Mattmüller","slug":"robert-mattmuller","given":"Robert","family":"Mattmüller","affiliation":""}],"abstract":"Abstraction heuristics are a popular method to guide optimal search algorithms in classical planning. Cost partitionings allow to sum heuristic estimates admissibly by distributing action costs among the heuristics. We introduce state-dependent cost partitionings which take context information of actions into account, and show that an optimal state-dependent cost partitioning dominates its state-independent counterpart. We demonstrate the potential of our idea with a state-dependent variant of the recently proposed saturated cost partitioning, and show that it has the potential to improve not only over its state-independent counterpart, but even over the optimal state-independent cost partitioning. Our empirical results give evidence that ignoring the context of actions in the computation of a cost partitioning leads to a significant loss of information.","doi":null,"url":"https://ijcai.org/proceedings/2016/448","pdf":"https://ijcai.org/proceedings/2016/0448.pdf","markdown":"https://ijcai.org/proceedings/2016/448.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/448","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":448,"pages":{"first":3161,"last":3168},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}