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

doi:10.1609/aaai.v33i01.33017562

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 journal › Conference paper › peer-review

4 Citations (Scopus)

363 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 language	English
Pages (from-to)	7562-7569
Journal	Proceedings of the AAAI Conference on Artificial Intelligence
Volume	33
Issue number	1
Early online date	17 Jul 2019
DOIs	https://doi.org/10.1609/aaai.v33i01.33017562
Publication status	Published - Jul 2019
Event	Thirty-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 2019 → 1 Feb 2019

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10.1609/aaai.v33i01.33017562Licence: Unspecified

intervalconstraintsSubmitted manuscript, 295 KB
4748-Article Text COLES July2019Final published version, 839 KBLicence: Unspecified

AI Planning with Continuous Non-Linear Change
Coles, A. (Primary Investigator)
EPSRC Engineering and Physical Sciences Research Council
1/01/2017 → 30/06/2018
Project: Research
European Robotic Goal-Oriented autonomous controller : ERGO
Coles, A. (Primary Investigator), Coles, A. (Co-Investigator) & Long, D. (Co-Investigator)
EC European Commission
1/11/2016 → 31/01/2019
Project: Research

Cite this

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title = "Efficiently Reasoning with Interval Constraints in Forward Search Planning",

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.",

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doi = "10.1609/aaai.v33i01.33017562",

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T1 - Efficiently Reasoning with Interval Constraints in Forward Search Planning

AU - Coles, Amanda

AU - Coles, Andrew

AU - Martinez, Moises

AU - Savas, Emre

AU - Delfa, Juan Manuel

AU - de la Rosa, Tomás

AU - E-Martín, Yolanda

AU - García Olaya, Angel

PY - 2019/7

Y1 - 2019/7

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AB - 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.

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DO - 10.1609/aaai.v33i01.33017562

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JO - Proceedings of the AAAI Conference on Artificial Intelligence

JF - Proceedings of the AAAI Conference on Artificial Intelligence

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Y2 - 27 January 2019 through 1 February 2019

ER -

Efficiently Reasoning with Interval Constraints in Forward Search Planning

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Projects

AI Planning with Continuous Non-Linear Change

European Robotic Goal-Oriented autonomous controller : ERGO

Cite this