Planning in the Dark: LLM-Symbolic Planning Pipeline without Experts
<|reference_start|>Planning in the Dark: LLM-Symbolic Planning Pipeline without Experts: Large Language Models (LLMs) have shown promise in solving natural language-described planning tasks, but their direct use often leads to inconsistent reasoning and hallucination. While hybrid LLM-symbolic planning pipelines have emerged as a more robust alternative, they typically require extensive expert intervention to refine and validate generated action schemas. It not only limits scalability but also introduces a potential for biased interpretation, as a single expert's interpretation of ambiguous natural language descriptions might not align with the user's actual intent. To address this, we propose a novel approach that constructs an action schema library to generate multiple candidates, accounting for the diverse possible interpretations of natural language descriptions. We further introduce a semantic validation and ranking module that automatically filter and rank the generated schemas and plans without expert-in-the-loop. The experiments showed our pipeline maintains superiority in planning over the direct LLM planning approach. These findings demonstrate the feasibility of a fully automated end-to-end LLM-symbolic planner that requires no expert intervention, opening up the possibility for a broader audience to engage with AI planning with less prerequisite of domain expertise.<|reference_end|>
@article{huang2024planning,
    title={Planning in the Dark: LLM-Symbolic Planning Pipeline without Experts},
    author={Sukai Huang, Nir Lipovetzky and Trevor Cohn},
    journal={arXiv preprint arXiv:2409.15915},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15915},
    primaryClass={cs.AI}
}