{"type":"ScholarlyArticle","title":"Symbolic Merge-and-Shrink for Cost-Optimal Planning","authors":[{"name":"Álvaro Torralba Arias de Reyna","slug":"alvaro-torralba-arias-de-reyna","given":"Álvaro Torralba Arias","family":"de Reyna","affiliation":""},{"name":"Carlos Linares López","slug":"carlos-linares-lopez","given":"Carlos","family":"Linares López","affiliation":""},{"name":"Daniel Borrajo","slug":"daniel-borrajo","given":"Daniel","family":"Borrajo","affiliation":""}],"abstract":"Symbolic PDBs and Merge-and-Shrink (M&S) are two approaches to derive admissible heuristics for optimal planning. We present a combination of these techniques, Symbolic Merge-and-Shrink (SM&S), which uses M&S abstractions as a relaxation criterion for a symbolic backward search. Empirical evaluation shows that SM&S has the strengths of both techniques deriving heuristics at least as good as the best of them for most domains.","doi":null,"url":"https://ijcai.org/proceedings/2013/352","pdf":"https://ijcai.org/proceedings/2013/0352.pdf","markdown":"https://ijcai.org/proceedings/2013/352.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/352","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":352,"pages":{"first":2394,"last":2399},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}