Temporal Planning with Incomplete Knowledge and Perceptual Information

Yaniel Carreno
(Edinburgh Centre for Robotics)
Yvan Petillot
(Heriot-Watt University)
Ronald P. A. Petrick
(Heriot-Watt University)

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.

In Rafael C. Cardoso, Angelo Ferrando, Fabio Papacchini, Mehrnoosh Askarpour and Louise A. Dennis: Proceedings of the Second Workshop on Agents and Robots for reliable Engineered Autonomy (AREA 2022), Vienna, Austria, 24th July 2022, Electronic Proceedings in Theoretical Computer Science 362, pp. 37–53.
Published: 20th July 2022.

ArXived at: http://dx.doi.org/10.4204/EPTCS.362.6 bibtex PDF
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