Temporal Planning with Incomplete Knowledge and Perceptual Information
This addresses the need for more flexible AI planners in real-world applications, though it appears incremental as it builds on existing PDDL frameworks.
The paper tackles the problem of temporal planning with incomplete knowledge, sensing, and numeric constraints by proposing a new approach that combines contingent plan construction within a temporal framework, and it demonstrates good performance on new evaluation domains.
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.