@inproceedings{gong-etal-2026-iterative,
title = "Iterative Formalization and Planning in Partially Observable Environments",
author = "Gong, Liancheng and
Zhu, Wang Bill and
Thomason, Jesse and
Zhang, Li",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.620/",
pages = "12755--12783",
ISBN = "979-8-89176-395-1",
abstract = "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. While most existing methodology only applies to fully observable environments, we adapt to the more realistic and challenging partially observable environments without sufficient information to make a complete plan. We propose PDDLego+, a framework to iteratively formalize, plan, grow, and refine PDDL representations by decomposing the environment and the goal into fully observable episodes. Without fine-tuning, in-context exemplars, or trajectories, PDDLego+ improves planning success and exhibits robustness against problem complexity compared to end-to-end approaches. We also show that the domain knowledge captured after a successful trial can benefit future tasks."
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<abstract>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. While most existing methodology only applies to fully observable environments, we adapt to the more realistic and challenging partially observable environments without sufficient information to make a complete plan. We propose PDDLego+, a framework to iteratively formalize, plan, grow, and refine PDDL representations by decomposing the environment and the goal into fully observable episodes. Without fine-tuning, in-context exemplars, or trajectories, PDDLego+ improves planning success and exhibits robustness against problem complexity compared to end-to-end approaches. We also show that the domain knowledge captured after a successful trial can benefit future tasks.</abstract>
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%0 Conference Proceedings
%T Iterative Formalization and Planning in Partially Observable Environments
%A Gong, Liancheng
%A Zhu, Wang Bill
%A Thomason, Jesse
%A Zhang, Li
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F gong-etal-2026-iterative
%X 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. While most existing methodology only applies to fully observable environments, we adapt to the more realistic and challenging partially observable environments without sufficient information to make a complete plan. We propose PDDLego+, a framework to iteratively formalize, plan, grow, and refine PDDL representations by decomposing the environment and the goal into fully observable episodes. Without fine-tuning, in-context exemplars, or trajectories, PDDLego+ improves planning success and exhibits robustness against problem complexity compared to end-to-end approaches. We also show that the domain knowledge captured after a successful trial can benefit future tasks.
%U https://aclanthology.org/2026.findings-acl.620/
%P 12755-12783
Markdown (Informal)
[Iterative Formalization and Planning in Partially Observable Environments](https://aclanthology.org/2026.findings-acl.620/) (Gong et al., Findings 2026)
ACL