On the Combination of Classical Knowledge Engineering Tools and LLMs to Build Automated Planning Models

Automated Planning (AP) is a problem-solving technique applicable to a wide range of scenarios and goals. It typically requires a complete and accurate description of the planning task expressed in a formal language to generate a solution plan that achieves the goals. However, creating these descriptions can be time-consuming and error-prone, often resulting in unsolvable planning tasks. Planning systems often lack the ability to explain why a task is deemed unsolvable. In this work, we present an integrated knowledge engineering system that allows users to graphically depict AP use cases using transition diagrams, which are automatically converted into a formal language. To facilitate the debugging process, we propose connecting the system with large language models (LLMs) to explore their capabilities in assisting with flawed planning tasks, fixing the model and making the tasks solvable.