PDDLEGO: Iterative Planning in Textual Environments
<|reference_start|>PDDLEGO: Iterative Planning in Textual Environments: 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%).<|reference_end|>
@article{zhang2024pddlego:,
    title={PDDLEGO: Iterative Planning in Textual Environments},
    author={Li Zhang, Peter Jansen, Tianyi Zhang, Peter Clark, Chris
  Callison-Burch, Niket Tandon},
    journal={arXiv preprint arXiv:2405.19793},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19793},
    primaryClass={cs.CL}
}