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Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
PDDLEGO: Iterative Planning in Textual Environments
30 May 2024
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%).
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Details
- Title
- PDDLEGO: Iterative Planning in Textual Environments
- Creators
- Li Zhang - Drexel University, Computer SciencePeter JansenTianyi ZhangPeter ClarkChris Callison-BurchNiket Tandon
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Computer Science
- Other Identifier
- 991022043073004721