Efficiently Reasoning with Interval Constraints in Forward Search Planning


  • Amanda Coles King's College London
  • Andrew Coles King's College London
  • Moises Martinez King's College London
  • Emre Savas King's College London
  • Juan Manuel Delfa European Space Agency
  • Tomás de la Rosa Universidad Carlos III de Madrid
  • Yolanda E-Martín Universidad Carlos III de Madrid
  • Angel García-Olaya Universidad Carlos III de Madrid




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.




How to Cite

Coles, A., Coles, A., Martinez, M., Savas, E., Delfa, J. M., de la Rosa, T., E-Martín, Y., & García-Olaya, A. (2019). Efficiently Reasoning with Interval Constraints in Forward Search Planning. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 7562-7569. https://doi.org/10.1609/aaai.v33i01.33017562



AAAI Technical Track: Planning, Routing, and Scheduling