pddl+ is an expressive planning formalism that enables the modelling of hybrid domains with both discrete and continuous dynamics. However, its expressiveness makes this language notoriously difficult to handle natively. To address this challenge, translations from time-discrete pddl+ into numeric pddl2.1 have been proposed as a way to reframe the rich expressiveness of pddl+ into a simpler and more manageable formalism. In this work, we first analyse existing translations and provide a means to compare them in terms of induced state space and the size of the reformulated tasks. Secondly, we propose a novel translation leveraging the structure of the problem to generate a compact reformulation. Our experimental results indicate that the novel translation outperforms the existing ones on a range of benchmarks.

A Structure-Sensitive Translation from Hybrid to Numeric Planning

Percassi F.;Scala E.;Vallati M.
2023-01-01

Abstract

pddl+ is an expressive planning formalism that enables the modelling of hybrid domains with both discrete and continuous dynamics. However, its expressiveness makes this language notoriously difficult to handle natively. To address this challenge, translations from time-discrete pddl+ into numeric pddl2.1 have been proposed as a way to reframe the rich expressiveness of pddl+ into a simpler and more manageable formalism. In this work, we first analyse existing translations and provide a means to compare them in terms of induced state space and the size of the reformulated tasks. Secondly, we propose a novel translation leveraging the structure of the problem to generate a compact reformulation. Our experimental results indicate that the novel translation outperforms the existing ones on a range of benchmarks.
2023
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
MIUR (compresi PRIN FIRB,FISR)
Inglese
22nd International Conference of the Italian Association for Artificial Intelligence, AIxIA 2023
2023
ita
14318
105
118
14
9783031475450
9783031475467
Springer Science and Business Media Deutschland GmbH
AI Planning; Hybrid Planning; Model Translation
no
Not applicable
none
Percassi, F.; Scala, E.; Vallati, M.
273
info:eu-repo/semantics/conferenceObject
3
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/597211
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