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Decidability and complexity of action-based temporal planning over dense time

Gigante, Nicola
•
Micheli, Andrea
•
Montanari, Angelo
•
Scala, Enrico
2022
  • journal article

Periodico
ARTIFICIAL INTELLIGENCE
Abstract
In this paper, we study the computational complexity of action-based temporal planning interpreted over dense time. When time is assumed to be 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, focusing on some particularly 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 it is allowed, it becomes EXPSPACE-complete with ε-separation and even undecidable with non-zero separation. These results clarify the computational consequences of different choices in the definition at the core of the PDDL 2.1 semantics, which have been vague until now.
DOI
10.1016/j.artint.2022.103686
Archivio
http://hdl.handle.net/11390/1222051
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85126552138
https://www.sciencedirect.com/science/article/pii/S0004370222000261
https://ricerca.unityfvg.it/handle/11390/1222051
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Soggetti
  • Temporal planningComp...

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