Decidability and Complexity of Action-Based Temporal Planning over Dense Time

This paper studies the computational complexity of temporal planning, as represented by PDDL 2.1, interpreted over dense time. When time is considered discrete, the problem is known to be EXPSPACE-complete. However, the official PDDL 2.1 semantics, and many implementations, interpret time as a dense domain. This work provides several results about the complexity of the problem, studying a few interesting cases: whether a minimum amount ϵ of separation between mutually exclusive events is given, in contrast to the separation being simply required to be non-zero, and whether or not actions are allowed to overlap already running instances of themselves. We prove the problem to be PSPACE-complete when self-overlap is forbidden, whereas, when allowed, it becomes EXPSPACE-complete with ϵ-separation and undecidable with non-zero separation. These results clarify the computational consequences of different choices in the definition of the PDDL 2.1 semantics, which were vague until now.

[1]  David Wang,et al.  tBurton: A Divide and Conquer Temporal Planner , 2015, AAAI.

[2]  Sergiy Bogomolov,et al.  Temporal Planning as Refinement-Based Model Checking , 2019, ICAPS.

[3]  Sergiy Bogomolov,et al.  PDDL+ Planning with Hybrid Automata: Foundations of Translating Must Behavior , 2015, ICAPS.

[4]  Robert Mattmüller,et al.  Using the Context-enhanced Additive Heuristic for Temporal and Numeric Planning , 2009, ICAPS.

[5]  Angelo Montanari,et al.  Complexity of Timeline-Based Planning , 2017, ICAPS.

[6]  Masood Feyzbakhsh Rankooh,et al.  ITSAT: An Efficient SAT-Based Temporal Planner , 2015, J. Artif. Intell. Res..

[7]  Angelo Montanari,et al.  Decidability and Complexity of Timeline-Based Planning over Dense Temporal Domains , 2018, KR.

[8]  Scott Sanner,et al.  A Survey of the Seventh International Planning Competition , 2012, AI Mag..

[9]  P. van Emde Boas The convenience of tiling , 1996 .

[10]  Angelo Montanari,et al.  Timelines Are Expressive Enough to Capture Action-Based Temporal Planning , 2016, 2016 23rd International Symposium on Temporal Representation and Reasoning (TIME).

[11]  Tom Bylander,et al.  The Computational Complexity of Propositional STRIPS Planning , 1994, Artif. Intell..

[12]  Patricia Bouyer,et al.  Updatable timed automata , 2004, Theor. Comput. Sci..

[13]  Jussi Rintanen,et al.  Complexity of Concurrent Temporal Planning , 2007, ICAPS.

[14]  Rajeev Alur,et al.  A Theory of Timed Automata , 1994, Theor. Comput. Sci..

[15]  Stefan Edelkamp,et al.  Automated Planning: Theory and Practice , 2007, Künstliche Intell..

[16]  Andrew Coles,et al.  Forward-Chaining Partial-Order Planning , 2010, ICAPS.

[17]  Ivan Serina,et al.  Planning Through Stochastic Local Search and Temporal Action Graphs in LPG , 2003, J. Artif. Intell. Res..

[18]  Paolo Traverso,et al.  Automated Planning and Acting , 2016 .

[19]  Subbarao Kambhampati,et al.  Sapa: A Multi-objective Metric Temporal Planner , 2003, J. Artif. Intell. Res..

[20]  Maria Fox,et al.  PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains , 2003, J. Artif. Intell. Res..

[21]  Ji-Ae Shin,et al.  Processes and continuous change in a SAT-based planner , 2005, Artif. Intell..

[22]  James A. Hendler,et al.  Complexity results for HTN planning , 1994, Annals of Mathematics and Artificial Intelligence.

[23]  Lukás Chrpa,et al.  The 2014 International Planning Competition: Progress and Trends , 2015, AI Mag..

[24]  Subbarao Kambhampati,et al.  When is Temporal Planning Really Temporal? , 2007, IJCAI.