PDDLEGO: Iterative Planning in Textual Environments

Li Zhang, Peter Jansen, Tianyi Zhang, Peter Clark, Chris Callison-Burch, Niket Tandon

Abstract
Planning in textual environments have been shown to be a long-standing challenge even for current models. A recent, promising line of work uses LLMs to generate a formal representation of the environment that can be solved by a symbolic planner. However, existing methods rely on a fully-observed environment where all entity states are initially known, so a one-off representation can be constructed, leading to a complete plan. In contrast, we tackle partially-observed environments where there is initially no sufficient information to plan for the end-goal. We propose PDDLEGO that iteratively construct a planning representation that can lead to a partial plan for a given sub-goal. By accomplishing the sub-goal, more information is acquired to augment the representation, eventually achieving the end-goal. We show that plans produced by few-shot PDDLEGO are 43% more efficient than generating plans end-to-end on the Coin Collector simulation, with strong performance (98%) on the more complex Cooking World simulation where end-to-end LLMs fail to generate coherent plans (4%).
Anthology ID:
2024.starsem-1.17
Volume:
Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Danushka Bollegala, Vered Shwartz
Venue:
*SEM
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
212–221
Language:
URL:
https://aclanthology.org/2024.starsem-1.17
DOI:
Bibkey:
Cite (ACL):
Li Zhang, Peter Jansen, Tianyi Zhang, Peter Clark, Chris Callison-Burch, and Niket Tandon. 2024. PDDLEGO: Iterative Planning in Textual Environments. In Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024), pages 212–221, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
PDDLEGO: Iterative Planning in Textual Environments (Zhang et al., *SEM 2024)
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PDF:
https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.starsem-1.17.pdf
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