@article{Coles_Coles_Martinez_Savas_Delfa_de la Rosa_E-Martín_García-Olaya_2019, title={Efficiently Reasoning with Interval Constraints in Forward Search Planning}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/4748}, DOI={10.1609/aaai.v33i01.33017562}, abstractNote={<p>In 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.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Coles, Amanda and Coles, Andrew and Martinez, Moises and Savas, Emre and Delfa, Juan Manuel and de la Rosa, Tomás and E-Martín, Yolanda and García-Olaya, Angel}, year={2019}, month={Jul.}, pages={7562-7569} }