Towards More Likely Models for AI Planning

Turgay Caglar, Sirine Belhaj, Tathagata Chakraborti, Michael Katz, Sarath Sreedharan

Keywords: Model Space Search, Large language models

Abstract:

This is the first work to look at the application of large language models (LLMs) for model space edits in automated planning tasks. We look at two quintessential model-space reasoning tasks: unsolvability and explanations. We empirically demonstrate how the performance of an LLM contrasts with combinatorial search (CS) -- an approach that has been traditionally used to solve model space tasks in planning --
with the increasing complexity of model edits and the increasing complexity of plans, both with the LLM in the role of a standalone model-space reasoner as well as in concert with the CS approach as part of a two-stage process. Our experiments show promising results suggesting further forays of LLMs into the exciting world of model space reasoning for planning tasks in the future.

Submission Number: 23