@inproceedings{
caglar2023towards,
title={Towards More Likely Models for {AI} Planning},
author={Turgay Caglar and Sirine Belhaj and Tathagata Chakraborti and Michael Katz and Sarath Sreedharan},
booktitle={NeurIPS 2023 Workshop on Generalization in Planning},
year={2023},
url={https://openreview.net/forum?id=eIROHscxeg}
}
This is the first work to look at the application of large language models (LLMs) for the purpose of model space edits in automated planning tasks. To set the stage for this sangam, we start by enumerating the different flavors of model space problems that have been studied so far in the AI planning literature and explore the effect of an LLM on those tasks with detailed illustrative examples. We also 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, both with the LLM in the role of a standalone model space reasoner as well as in the role of a statistical modeling tool 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.
Towards More Likely Models for AI Planning
Turgay Caglar Sirine Belhaj Tathagata Chakraborti Michael Katz Sarath Sreedharan