# Generating consistent PDDL domains with Large Language Models

arXiv: https://arxiv.org/abs/2404.07751 · HTML: https://mlsanity.com/paper/2404.07751/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://mlsanity.com/paper/2209.11302/) (2022)
- [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://mlsanity.com/paper/2212.04088/) (2022)
- [Text2Motion: From Natural Language Instructions to Feasible Plans](https://mlsanity.com/paper/2303.12153/) (2023)
- [Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning](https://mlsanity.com/paper/2305.14909/) (2023)
- [OpenAgents: An Open Platform for Language Agents in the Wild](https://mlsanity.com/paper/2310.10634/) (2023)
- [Translating Natural Language to Planning Goals with Large-Language Models](https://mlsanity.com/paper/2302.05128/) (2023)
- [From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought](https://mlsanity.com/paper/2306.12672/) (2023)
- [AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers](https://mlsanity.com/paper/2306.06531/) (2023)
- [Grammar Prompting for Domain-Specific Language Generation with Large Language Models](https://mlsanity.com/paper/2305.19234/) (2023)
- [CoPAL: Corrective Planning of Robot Actions with Large Language Models](https://mlsanity.com/paper/2310.07263/) (2023)
- [CAPE: Corrective Actions from Precondition Errors using Large Language Models](https://mlsanity.com/paper/2211.09935/) (2022)
- [From Cooking Recipes to Robot Task Trees -- Improving Planning Correctness and Task Efficiency by Leveraging LLMs with a Knowledge Network](https://mlsanity.com/paper/2309.09181/) (2023)
- [Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability](https://mlsanity.com/paper/2401.07868/) (2024)

## Built on by (most cited first, 15 of 15)

- [A Survey on Large Language Models for Automated Planning](https://mlsanity.com/paper/2502.12435/) (2025)
- [Learning Compositional Behaviors from Demonstration and Language](https://mlsanity.com/paper/2505.21981/) (2025)
- [Generating Symbolic World Models via Test-time Scaling of Large Language Models](https://mlsanity.com/paper/2502.04728/) (2025)
- [Learning Abstractions for Hierarchical Planning in Program-Synthesis Agents](https://mlsanity.com/paper/2602.00929/) (2026)
- [Aligning LLM+PDDL Symbolic Plans with Human Objective Specifications through Evolutionary Algorithm Guidance](https://mlsanity.com/paper/2412.00300/) (2024)
- [Abstracting Situation Calculus Action Theories](https://mlsanity.com/paper/2410.14712/) (2024)
- [Learning Hierarchical Domain Models Through Environment-Grounded Interaction](https://mlsanity.com/paper/2505.13497/) (2025)
- [PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models](https://mlsanity.com/paper/2506.20097/) (2025)
- [Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design](https://mlsanity.com/paper/2509.22834/) (2025)
- [Agentic LLM Planning via Step-Wise PDDL Simulation: An Empirical Characterisation](https://mlsanity.com/paper/2603.06064/) (2026)
- [Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding](https://mlsanity.com/paper/2605.09271/) (2026)
- [Model-Based AI planning and Execution Systems for Robotics](https://mlsanity.com/paper/2505.04493/) (2025)
- [Grounded Vision-Language Interpreter for Long-Horizon Bimanual Task and Motion Planning](https://mlsanity.com/paper/2506.03270/) (2025)
- [Enhancing Cognitive Robotics with Commonsense through LLM-Generated Preconditions and Subgoals](https://mlsanity.com/paper/2512.00069/) (2025)
- [CVE2AP: Automated Generation of PDDL-Encoded Attack Paths via Large Language Models](https://mlsanity.com/paper/2610.03383/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are capable of transforming natural language domain descriptions into plausibly looking PDDL markup. However, ensuring that actions are consistent within domains still remains a challenging task. In this paper we present a novel concept to significantly improve the quality of LLM-generated PDDL models by performing automated consistency checking during the generation process. Although the proposed consistency checking strategies still can't guarantee absolute correctness of generated models, they can serve as valuable source of feedback reducing the amount of correction efforts expected from a human in the loop. We demonstrate the capabilities of our error detection approach on a number of classical and custom planning domains (logistics, gripper, tyreworld, household, pizza).