PDDLEGO: Iterative Planning in Textual Environments

• URL: http://arxiv.org/abs/2405.19793v2
• Date: Fri, 9 Aug 2024 14:18:23 GMT
• Title: PDDLEGO: Iterative Planning in Textual Environments
• Authors: Li Zhang, Peter Jansen, Tianyi Zhang, Peter Clark, Chris Callison-Burch, Niket Tandon,
• Abstract summary: Planning in textual environments has been shown to be a long-standing challenge even for current models. We propose PDDLEGO that iteratively construct a planning representation that can lead to a partial plan for a given sub-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.
• Score: 56.12148805913657
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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