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Generating consistent PDDL domains with Large Language Models

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arxiv 2404.07751 v1 pith:7QBGYR3Y submitted 2024-04-11 cs.RO cs.AI

Pavel Smirnov , Frank Joublin , Antonello Ceravola , Michael Gienger This is my paper · ORCID
classification cs.ROcs.AI
keywords modelsdomainslanguagepddlcheckingconsistencyconsistentlarge
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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).

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning Compositional Behaviors from Demonstration and Language

    cs.RO 2025-05 conditional novelty 6.0 of 10

    BLADE learns structured, planable action representations from language-annotated demonstrations and composes them with a symbolic planner, outperforming latent and LLM/VLM baselines on new manipulation tasks.

  2. Generating Symbolic World Models via Test-time Scaling of Large Language Models

    cs.AI 2025-02 conditional novelty 5.0 of 10

    Best-of-N sampling plus iterative self-critique, called iVML, lets a 7B open LLM generate PDDL planning domains with over 85% and 71% success on two benchmarks, outperforming o1-mini.

  3. Code-Driven Planning in Grid Worlds with Large Language Models

    cs.AI 2025-05 conditional novelty 4.0 of 10

    An iterative code-generation framework (IPP) improves LLM performance on GRASP and MiniGrid grid-planning tasks by refining generated policy programs based on execution feedback.

  4. Model-Based AI planning and Execution Systems for Robotics

    cs.RO 2025-05 conditional novelty 1.0 of 10

    A structured survey of model-based AI planning and execution systems for robotics, comparing ROSPlan, CLIPS Executive, PlanSys2, SkiROS2, the Skill-Based Architecture, and AOS, and proposing a future research agenda.

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