Efficiently Reasoning with Interval Constraints in Forward Search Planning

Amanda Coles, Andrew Coles, Moises Martinez, Emre Savas, Juan Manuel Delfa, Tomás de la Rosa, Yolanda E-Martín, Angel García Olaya

Research output: Contribution to journalConference paperpeer-review

4 Citations (Scopus)
323 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)7562-7569
JournalProceedings of the AAAI Conference on Artificial Intelligence
Volume33
Issue number1
Early online date17 Jul 2019
DOIs
Publication statusPublished - Jul 2019
EventThirty-Third AAAI Conference on Artificial Intelligence
Thirty-First Conference on Innovative Applications of Artificial Intelligence
The Ninth Symposium on Educational Advances in Artificial Intelligence
- Hilton Hawaiian Village, Honolulu, Hawaii, United States
Duration: 27 Jan 20191 Feb 2019

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