Papers with Planning Domain Definition Language
On the Limit of Language Models as Planning Formalizers (2025.acl-long)
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| Challenge: | Large Language Models can create plans that are neither executable nor verifiable in grounded environments. |
| Approach: | They use Large Language Models to generate a formal representation of the planning domain in some language, such as Planning Domain Definition Language (PDDL). |
| Outcome: | The proposed model outperforms the models directly generating plans while being robust to lexical perturbation. |
Iterative Formalization and Planning in Partially Observable Environments (2026.findings-acl)
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| Challenge: | Existing methods to formalize an environment into the Planning Domain Definition Language (PDDL) have been shown to improve performance and control. |
| Approach: | They propose a framework to iteratively formalize, plan, grow, and refine PDDL representations by decomposing the environment and the goal into fully observable episodes. |
| Outcome: | The proposed framework improves planning success and exhibits robustness against problem complexity compared to end-to-end approaches. |
Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards (2026.acl-long)
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| Challenge: | Existing PRM datasets are expensive to construct and limited to the mathematical domain. |
| Approach: | They propose a method to generate a corpus of one million reasoning steps using the Planning Domain Definition Language. |
| Outcome: | The proposed model generates a corpus of approximately one million reasoning steps across various PDDL domains and trains them. |