In this paper, we investigate non-Markovian Nondeterministic Fully Observable Planning Domains (NMFONDs), variants of Nondeterministic Fully Observable Planning Domains (FONDs) where the next state is determined by the full history leading to the current state. In particular, we introduce TFONDs which are NMFONDs where conditions on the history are succinctly and declaratively specified using the linear-time temporal logic on finite traces LTLf and its extension LDLf. We provide algorithms for planning in TFONDs for general LTLf/LDLf goals, and establish tight complexity bounds w.r.t. the domain representation and the goal, separately. We also show that TFONDs are able to capture all NMFONDs in which the dependency on the history is “finite state”. Finally, we show that TFONDs also capture Partially Observable Nondeterministic Planning Domains (PONDs), but without referring to unobservable variables.

Planning for LTLF/LDLF goals in non-Markovian fully observable nondeterministic domains / Brafman, R. I.; De Giacomo, G.. - In: IJCAI. - ISSN 1045-0823. - (2019), pp. 1602-1608. (Intervento presentato al convegno 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 tenutosi a Macao; China).

Planning for LTLF/LDLF goals in non-Markovian fully observable nondeterministic domains

Brafman R. I.
;
De Giacomo G.
2019

Abstract

In this paper, we investigate non-Markovian Nondeterministic Fully Observable Planning Domains (NMFONDs), variants of Nondeterministic Fully Observable Planning Domains (FONDs) where the next state is determined by the full history leading to the current state. In particular, we introduce TFONDs which are NMFONDs where conditions on the history are succinctly and declaratively specified using the linear-time temporal logic on finite traces LTLf and its extension LDLf. We provide algorithms for planning in TFONDs for general LTLf/LDLf goals, and establish tight complexity bounds w.r.t. the domain representation and the goal, separately. We also show that TFONDs are able to capture all NMFONDs in which the dependency on the history is “finite state”. Finally, we show that TFONDs also capture Partially Observable Nondeterministic Planning Domains (PONDs), but without referring to unobservable variables.
2019
28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Planning; Artificial intelligence; AI planning
04 Pubblicazione in atti di convegno::04c Atto di convegno in rivista
Planning for LTLF/LDLF goals in non-Markovian fully observable nondeterministic domains / Brafman, R. I.; De Giacomo, G.. - In: IJCAI. - ISSN 1045-0823. - (2019), pp. 1602-1608. (Intervento presentato al convegno 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 tenutosi a Macao; China).
File allegati a questo prodotto
File Dimensione Formato  
Brafman_Planning_2019.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 165.73 kB
Formato Adobe PDF
165.73 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1383988
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 21
  • ???jsp.display-item.citation.isi??? 3
social impact