A Sound (But Incomplete) Polynomial Translation from Discretised PDDL+ to Numeric Planning

Francesco Percassi; Enrico Scala; Mauro Vallati

doi:10.1007/978-3-031-08421-8_2

A Sound (But Incomplete) Polynomial Translation from Discretised PDDL+ to Numeric Planning

Francesco Percassi, Enrico Scala, Mauro Vallati

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

PDDL+ is an expressive planning formalism that enables the modelling of domains having both discrete and continuous dynamics. Recently, two mappings for translating discretised PDDL+ problems into a numeric a-temporal task have been proposed. Such translations produce a task of exponential or polynomial size w.r.t. the size of the native task. In this work, starting from the above-mentioned polynomial translation, we introduce a sound but not generally complete variant that has the potential to improve the performance of numeric planning engines. We define the subclass of problems where the variant is safely applicable, and we assess the advantages of such a translation.

Original language	English
Title of host publication	AIxIA 2021 – Advances in Artificial Intelligence
Subtitle of host publication	20th International Conference of the Italian Association for Artificial Intelligence, Virtual Event, December 1–3, 2021, Revised Selected Papers
Editors	Stefania Bandini, Francesca Gasparini, Viviana Mascardi, Matteo Palmonari, Giuseppe Vizzari
Publisher	Springer, Cham
Pages	18-31
Number of pages	14
Volume	LNCS/LNAI 13196
Edition	1st
ISBN (Electronic)	9783031084218
ISBN (Print)	9783031084201
DOIs	https://doi.org/10.1007/978-3-031-08421-8_2
Publication status	Published - 20 Jul 2022
Event	20th International Conference of the Italian Association for Artificial Intelligence - Online due to COVID-19 (originally planned to be held in Milano), Online Duration: 1 Dec 2021 → 3 Dec 2021 Conference number: 20 https://aixia2021.disco.unimib.it/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	LNCS/LNAI 13196
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference of the Italian Association for Artificial Intelligence
Abbreviated title	AIxIA 2021
City	Online
Period	1/12/21 → 3/12/21
Internet address	https://aixia2021.disco.unimib.it/

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10.1007/978-3-031-08421-8_2Licence: Unspecified

Cite this

Percassi, F., Scala, E., & Vallati, M. (2022). A Sound (But Incomplete) Polynomial Translation from Discretised PDDL+ to Numeric Planning. In S. Bandini, F. Gasparini, V. Mascardi, M. Palmonari, & G. Vizzari (Eds.), AIxIA 2021 – Advances in Artificial Intelligence: 20th International Conference of the Italian Association for Artificial Intelligence, Virtual Event, December 1–3, 2021, Revised Selected Papers (1st ed., Vol. LNCS/LNAI 13196, pp. 18-31). (Lecture Notes in Computer Science; Vol. LNCS/LNAI 13196). Springer, Cham. https://doi.org/10.1007/978-3-031-08421-8_2

Percassi, Francesco ; Scala, Enrico ; Vallati, Mauro. / A Sound (But Incomplete) Polynomial Translation from Discretised PDDL+ to Numeric Planning. AIxIA 2021 – Advances in Artificial Intelligence: 20th International Conference of the Italian Association for Artificial Intelligence, Virtual Event, December 1–3, 2021, Revised Selected Papers. editor / Stefania Bandini ; Francesca Gasparini ; Viviana Mascardi ; Matteo Palmonari ; Giuseppe Vizzari. Vol. LNCS/LNAI 13196 1st. ed. Springer, Cham, 2022. pp. 18-31 (Lecture Notes in Computer Science).

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abstract = "PDDL+ is an expressive planning formalism that enables the modelling of domains having both discrete and continuous dynamics. Recently, two mappings for translating discretised PDDL+ problems into a numeric a-temporal task have been proposed. Such translations produce a task of exponential or polynomial size w.r.t. the size of the native task. In this work, starting from the above-mentioned polynomial translation, we introduce a sound but not generally complete variant that has the potential to improve the performance of numeric planning engines. We define the subclass of problems where the variant is safely applicable, and we assess the advantages of such a translation.",

keywords = "Automated planning for hybrid systems, Planning via translation",

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Percassi, F, Scala, E & Vallati, M 2022, A Sound (But Incomplete) Polynomial Translation from Discretised PDDL+ to Numeric Planning. in S Bandini, F Gasparini, V Mascardi, M Palmonari & G Vizzari (eds), AIxIA 2021 – Advances in Artificial Intelligence: 20th International Conference of the Italian Association for Artificial Intelligence, Virtual Event, December 1–3, 2021, Revised Selected Papers. 1st edn, vol. LNCS/LNAI 13196, Lecture Notes in Computer Science, vol. LNCS/LNAI 13196, Springer, Cham, pp. 18-31, 20th International Conference of the Italian Association for Artificial Intelligence, Online, 1/12/21. https://doi.org/10.1007/978-3-031-08421-8_2

A Sound (But Incomplete) Polynomial Translation from Discretised PDDL+ to Numeric Planning. / Percassi, Francesco; Scala, Enrico; Vallati, Mauro.

AIxIA 2021 – Advances in Artificial Intelligence: 20th International Conference of the Italian Association for Artificial Intelligence, Virtual Event, December 1–3, 2021, Revised Selected Papers. ed. / Stefania Bandini; Francesca Gasparini; Viviana Mascardi; Matteo Palmonari; Giuseppe Vizzari. Vol. LNCS/LNAI 13196 1st. ed. Springer, Cham, 2022. p. 18-31 (Lecture Notes in Computer Science; Vol. LNCS/LNAI 13196).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Sound (But Incomplete) Polynomial Translation from Discretised PDDL+ to Numeric Planning

AU - Percassi, Francesco

AU - Scala, Enrico

AU - Vallati, Mauro

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N2 - PDDL+ is an expressive planning formalism that enables the modelling of domains having both discrete and continuous dynamics. Recently, two mappings for translating discretised PDDL+ problems into a numeric a-temporal task have been proposed. Such translations produce a task of exponential or polynomial size w.r.t. the size of the native task. In this work, starting from the above-mentioned polynomial translation, we introduce a sound but not generally complete variant that has the potential to improve the performance of numeric planning engines. We define the subclass of problems where the variant is safely applicable, and we assess the advantages of such a translation.

AB - PDDL+ is an expressive planning formalism that enables the modelling of domains having both discrete and continuous dynamics. Recently, two mappings for translating discretised PDDL+ problems into a numeric a-temporal task have been proposed. Such translations produce a task of exponential or polynomial size w.r.t. the size of the native task. In this work, starting from the above-mentioned polynomial translation, we introduce a sound but not generally complete variant that has the potential to improve the performance of numeric planning engines. We define the subclass of problems where the variant is safely applicable, and we assess the advantages of such a translation.

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KW - Planning via translation

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VL - LNCS/LNAI 13196

T3 - Lecture Notes in Computer Science

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EP - 31

BT - AIxIA 2021 – Advances in Artificial Intelligence

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A2 - Gasparini, Francesca

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PB - Springer, Cham

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ER -

Percassi F, Scala E, Vallati M. A Sound (But Incomplete) Polynomial Translation from Discretised PDDL+ to Numeric Planning. In Bandini S, Gasparini F, Mascardi V, Palmonari M, Vizzari G, editors, AIxIA 2021 – Advances in Artificial Intelligence: 20th International Conference of the Italian Association for Artificial Intelligence, Virtual Event, December 1–3, 2021, Revised Selected Papers. 1st ed. Vol. LNCS/LNAI 13196. Springer, Cham. 2022. p. 18-31. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-031-08421-8_2

A Sound (But Incomplete) Polynomial Translation from Discretised PDDL+ to Numeric Planning

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