A Collaborative Numeric Task Planning Framework based on Constraint Translations using LLMs

Anthony Favier, Ngoc La, Pulkit Verma, Julie Shah

Keywords: Human-AI Collaboration, Automated Planning, LLM, Task Planning, Interaction, Constraint, PDDL3, Numeric

Abstract:

Automated planning systems require formal constraint specifications that create significant barriers for domain experts not familiar with those formal specifications, thereby limiting the practical adoption of powerful planning tools in collaborative planning settings. To overcome this challenge, we propose an LLM-based pipeline to translate human natural language constraints into formal hard-trajectory constraints. The initial user input is first refined and decomposed into more explicit natural language constraints, both preparing constraints for formal encoding and offering a chance for the human to review and correct any misinterpretation. Then, the decomposed constraints are encoded into PDDL3. By integrating this with an automated planner, a graphical interface, and PDSim, we created a closed loop where the human gets plan simulations as feedback to their natural language constraints. This innovative collaborative planning framework enables users to leverage their intuition and expertise to intuitively guide automated planning without time-consuming programming expert interventions.
Through an ablation study, we demonstrate how our approach significantly improves the syntax and semantic accuracy of the translations compared to direct LLM translations. Our results demonstrate the potential of collaborative planning without technical expert interventions for higher-quality automated solving. On the other hand, our negative results seem to highlight the limitations of using PDDL3 constraints to leverage human high-level guidance as we expected, raising interesting reflections and potential discussions.

Paper Type: New Full Paper

Submission Number: 2