{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:04:53Z","timestamp":1784203493526,"version":"3.55.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:p>The Optimal In-Station Train Dispatching (InSTraDi) problem consists in commanding the movements of trains inside a railway station while both (i) respecting safety, time, and travel constraints and (ii) minimizing delays. \nIn Symbolic Pattern Planning (SPP), a pattern, suggesting the sequence of happenings to reach the goal, is encoded in a logic formula whose models correspond to valid plans. \nIf no valid plan is found, the pattern is extended until it covers a valid plan. However, plans of better quality could exist if we had continued extending the pattern.\nIn this paper, we formalize the InSTraDi problem as a Temporal Planning Task with Intermediate Conditions and Effects, and we show an InSTraDi-dependent way to construct, in polynomial time, a pattern ensuring the optimal plan can be found by the SPP approach without never extending the pattern. Analysis on realistic railway data validate our approach.<\/jats:p>","DOI":"10.24963\/kr.2026\/68","type":"proceedings-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:15:53Z","timestamp":1784200553000},"page":"723-733","source":"Crossref","is-referenced-by-count":0,"title":["Optimal In-Station Train Dispatching via Symbolic Pattern Planning"],"prefix":"10.24963","author":[{"given":"Matteo","family":"Cardellini","sequence":"first","affiliation":[{"name":"Universit\u00e0 degli Studi di Genova"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enrico","family":"Giunchiglia","sequence":"additional","affiliation":[{"name":"Universit\u00e0 degli Studi di Genova"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Davide","family":"Anguita","sequence":"additional","affiliation":[{"name":"Universit\u00e0 degli Studi di Genova"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmelo","family":"Lofiego","sequence":"additional","affiliation":[{"name":"Hitachi Rail STS"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Oneto","sequence":"additional","affiliation":[{"name":"Universit\u00e0 degli Studi di Genova"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pietro","family":"Ratto","sequence":"additional","affiliation":[{"name":"Hitachi Rail STS"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"23nd International Conference on Principles of Knowledge Representation and Reasoning {KR-2026}","theme":"Artificial Intelligence","location":"Lisbon, Portuagal","acronym":"KR-2026","number":"23","sponsor":["Artificial Intelligence Journal","Principles of Knowledge Representation and Reasoning Inc.","European Association for Artificial Intelligence"],"start":{"date-parts":[[2026,7,20]]},"end":{"date-parts":[[2026,7,18]]}},"container-title":["Proceedings of the TwentyThird International Conference on Principles of Knowledge Representation and Reasoning"],"original-title":[],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:16:12Z","timestamp":1784200572000},"score":1,"resource":{"primary":{"URL":"https:\/\/proceedings.kr.org\/2026\/68"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/kr.2026\/68","relation":{},"subject":[],"published":{"date-parts":[[2026,7]]}}}