Temporal Planning with Incomplete Knowledge and Perceptual Information

Yaniel Carreno*, Yvan Petillot, Ronald P. A. Petrick

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

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Abstract

In real-world applications, the ability to reason about incomplete knowledge, sensing, temporal notions, and numeric constraints is vital. While several AI planners are capable of dealing with some of these requirements, they are mostly limited to problems with specific types of constraints. This paper presents a new planning approach that combines contingent plan construction within a temporal planning framework, offering solutions that consider numeric constraints and incomplete knowledge. We propose a small extension to the Planning Domain Definition Language (PDDL) to model (i) incomplete, (ii) knowledge sensing actions that operate over unknown propositions, and (iii) possible outcomes from non-deterministic sensing effects. We also introduce a new set of planning domains to evaluate our solver, which has shown good performance on a variety of problems.

Original languageEnglish
Pages (from-to)37-53
Number of pages17
JournalElectronic Proceedings in Theoretical Computer Science
Volume362
DOIs
Publication statusPublished - 20 Jul 2022
Event2nd Workshop on Agents and Robots for Reliable Engineered Autonomy 2022 - Vienna, Austria
Duration: 24 Jul 2022 → …

ASJC Scopus subject areas

  • Software

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