Knowledge Engineering for Planning and Scheduling in the LLM Era

Abstract

Automated planning requires explicit domain knowledge, typically represented in PDDL, to generate effective solutions. The process of formulating, maintaining, and validating this knowledge is the cornerstone of Knowledge Engineering for Planning and Scheduling (KEPS). Although Large Language Models (LLMs) have shown promise for automated planning tasks, and are gaining popularity in the field, their impact on KEPS remains unexplored. In this paper we investigate the potential of LLMs to streamline and enhance the KEPS field, by taking a close look at the processes used to develop explicit symbolic knowledge models in safety-related applications. The paper's findings are that while LLMs can assist in knowledge acquisition and formulation, human domain expertise and external symbolic validators remain indispensable for ensuring correctness, operationality and completeness of planning applications.