Coles, AmandaColes, AndrewMartínez Muñoz, MoisesDelfa, Juan ManuelRosa Turbides, Tomás Eduardo de laEscudero Martín, YolandaGarcía Olaya, Ángel2020-05-202020-05-202019-01-27Garcia Olaya, Angel; De La Rosa Turbides, Tomas Eduardo; Escudero Martin, Yolanda; Coles, Amanda; Coles, Andrew; Martínez, Moises; Savas, Emre; Delfa, Juan Manuel (2019). Efficiently reasoning with interval constraints in forward search planning . Proceedings of the AAAI Conference on Artificial Intelligence. Estados Unidos De America: Aaai Press. Association For The Advancement Of Artificial Intelligence . Pp. 7562-756997815773580912159-5399https://hdl.handle.net/10016/3044827 de enero - 1 de febrero 2019, Hilton Hawaiian Village, Honolulu,Hawaii, USAIn this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting.engCopyright © 2019, Association for the Advancement of Artificial Intelligenceconference proceedingsInformáticaopen access756217569Proceedings of the AAAI Conference on Artificial Intelligence33CC/0000030442