{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:38:05Z","timestamp":1761176285702,"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>Recent work on learning for classical planning has primarily focused on exclusively employing the learned heuristics or policies. However, no purely learning-based method has consistently outperformed state-of-the-art planners to date. To address this, we return to the research paradigm that integrates learned domain knowledge with traditional, non-learned planning techniques. We propose a novel and simple approach for learning transition classifiers, using tree-based statistical learning over description logic features. In experiments, we evaluate various strategies for integrating learned classifiers with the FF heuristic, a state-of-the-art non-learned heuristic. Our results demonstrate that augmenting classical heuristics with transition classifiers leads to substantial performance improvements. The strongest variant combines classifier-based lookahead search with learned knowledge to avoid transitions into unsolvable states, frequently outperforming state-of-the-art traditional and learning-based planners.<\/jats:p>","DOI":"10.3233\/faia251375","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:59:53Z","timestamp":1761127193000},"source":"Crossref","is-referenced-by-count":0,"title":["Combining Heuristics and Transition Classifiers in Classical Planning"],"prefix":"10.3233","author":[{"given":"Farid","family":"Musayev","sequence":"first","affiliation":[{"name":"Link\u00f6ping University, Sweden. farid.musayev@liu.se, dominik.drexler@liu.se, daniel.gnad@liu.se, jendrik.seipp@liu.se"}]},{"given":"Dominik","family":"Drexler","sequence":"additional","affiliation":[{"name":"Link\u00f6ping University, Sweden. farid.musayev@liu.se, dominik.drexler@liu.se, daniel.gnad@liu.se, jendrik.seipp@liu.se"}]},{"given":"Daniel","family":"Gnad","sequence":"additional","affiliation":[{"name":"Link\u00f6ping University, Sweden. farid.musayev@liu.se, dominik.drexler@liu.se, daniel.gnad@liu.se, jendrik.seipp@liu.se"},{"name":"Heidelberg University, Germany"}]},{"given":"Jendrik","family":"Seipp","sequence":"additional","affiliation":[{"name":"Link\u00f6ping University, Sweden. farid.musayev@liu.se, dominik.drexler@liu.se, daniel.gnad@liu.se, jendrik.seipp@liu.se"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251375","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:59:54Z","timestamp":1761127194000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251375"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251375","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]]}}}