{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:38:11Z","timestamp":1761176291292,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Building a single Cartesian abstraction is usually not enough to obtain an informative heuristic for classical planning. Therefore, state-of-the-art methods decompose the original task into subtasks\u2014for example, one per goal atom\u2014and compute an abstraction for each individual subtask. However, building a single abstraction suffers from diminishing returns, while building multiple abstractions loses information about how to achieve the associated subtasks jointly. We interpolate between these two extremes by first considering subtasks individually and then merging some of the resulting abstractions. We introduce an efficient algorithm for merging pairs of Cartesian abstractions using their refinement hierarchies and show that it yields more informative abstractions in less time than a naive approach. Furthermore, we prove that adding merged abstractions can only improve a cost-partitioned heuristic based on saturated post-hoc optimization and that for maximal heuristic values, we need to keep the individual abstractions. Our experiments show that merging abstractions drastically improves the resulting heuristics.<\/jats:p>","DOI":"10.3233\/faia251384","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:00:04Z","timestamp":1761127204000},"source":"Crossref","is-referenced-by-count":0,"title":["Merging Cartesian Abstractions for Classical Planning"],"prefix":"10.3233","author":[{"given":"Mauricio","family":"Salerno","sequence":"first","affiliation":[{"name":"Universidad Carlos III de Madrid, Legan\u00e9s, Madrid, Spain"}]},{"given":"Raquel","family":"Fuentetaja","sequence":"additional","affiliation":[{"name":"Universidad Carlos III de Madrid, Legan\u00e9s, Madrid, Spain"}]},{"given":"David","family":"Speck","sequence":"additional","affiliation":[{"name":"University of Basel, Basel, Switzerland"}]},{"given":"Jendrik","family":"Seipp","sequence":"additional","affiliation":[{"name":"Link\u00f6ping University, Link\u00f6ping, Sweden"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251384","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:00:05Z","timestamp":1761127205000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251384"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251384","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}