KR2026Proceedings of the 23rd International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 23rd International Conference on Principles of Knowledge Representation and Reasoning

Lisbon, Portugal. July 20-23, 2026.

Edited by
  1. Renata Wassermann
  2. Marie-Laure Mugnier
  3. Franz Baader

ISSN: 2334-1033
ISBN: 978-1-956792-18-8

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Published by

Copyright © 2026 International Joint Conferences on Artificial Intelligence Organization

Optimal In-Station Train Dispatching via Symbolic Pattern Planning

  1. Matteo Cardellini(Università degli Studi di Genova)
  2. Enrico Giunchiglia(Università degli Studi di Genova)
  3. Davide Anguita(Università degli Studi di Genova)
  4. Carmelo Lofiego(Hitachi Rail STS)
  5. Luca Oneto(Università degli Studi di Genova)
  6. Pietro Ratto(Hitachi Rail STS)
PDF
BibTeX
https://doi.org/10.24963/kr.2026/68

Keywords

  1. null-Railway
  2. null-Transportation
  3. null-Planning
  4. null-Symbolic Pattern Planning

Abstract

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.

In 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.

If 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.

In 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.