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Zero-Shot Iterative Formalization and Planning in Partially Observable Environments
Preprint   Open access

Zero-Shot Iterative Formalization and Planning in Partially Observable Environments

Liancheng Gong, Wang Zhu, Jesse Thomason and Li Zhang
ArXiv.org
19 May 2025
url
https://arxiv.org/pdf/2505.13126View
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Abstract

Computer Science - Artificial Intelligence Computer Science - Computation and Language
Using LLMs not to predict plans but to formalize an environment into the Planning Domain Definition Language (PDDL) has been shown to improve performance and control. Existing work focuses on fully observable environments; we tackle the more realistic and challenging partially observable environments that lack of complete, reliable information. We propose PDDLego+, a framework to iteratively formalize, plan, grow, and refine PDDL representations in a zero-shot manner, without needing access to any existing trajectories. On two textual simulated environments, we show that PDDLego+ improves goal reaching success and exhibits robustness against problem complexity. We also show that the domain knowledge captured after a successful trial can benefit future tasks.

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