On the exploitation of control knowledge for enhancing automated planning

Abstract

Heuristic search provides an efficient way to automatically explore the state space in planning, while Control Knowledge (CK) also has the potential to significantly increase the performance of planners. Currently, most of the state-of-the-art planners primarily rely on sophisticated heuristic mechanisms. However, these planners fail to scale up and to provide (high-quality) solutions in a range of problems.

The objective of this paper is to incorporate CK with heuristic search in order to leverage the advantages of both, thus leading planners to achieve much higher efficiency. To achieve this, we introduce a novel CK which is specified by a variant of Linear Temporal Logic (LTL), referred to as LTL${}_P$. We propose an encoding methodology that translates LTL${}_P$ into standard planning models. Consequently, we can directly use existing heuristic planners to solve the augmented problem, and avoid tailoring the planners in order to deal with CK implicitly. The novelty of this approach lies in that we define a useful CK LTL${}_P$ with a concise encoding methodology, that can significantly improve the efficiency of heuristic search. In this paper, the encoding process is formally presented, and theoretical results on the complexity and soundness of the encoding are strictly proved. We find that appropriate CK is a good complement to heuristic search, and is capable of making hard problems easy to solve. Experiments demonstrate that our approach shows highly competitive results versus heuristic search and other CK-based techniques on many intractable benchmark problems, benefiting in improving the coverage and quality of plans.