mlsanityData to 7 Oct 2026

PROC2PDDL: Open-Domain Planning Representations from Texts

arXiv 2403.00092 ↗First posted 29 Feb 2024cs.CLcited by 15

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Abstract (arXiv)

Planning in a text-based environment continues to be a major challenge for AI systems. Recent approaches have used language models to predict a planning domain definition (e.g., PDDL) but have only been evaluated in closed-domain simulated environments. To address this, we present Proc2PDDL , the first dataset containing open-domain procedural texts paired with expert-annotated PDDL representations. Using this dataset, we evaluate state-of-the-art models on defining the preconditions and effects of actions. We show that Proc2PDDL is highly challenging, with GPT-3.5's success rate close to 0% and GPT-4's around 35%. Our analysis shows both syntactic and semantic errors, indicating LMs' deficiency in both generating domain-specific prgorams and reasoning about events. We hope this analysis and dataset helps future progress towards integrating the best of LMs and formal planning.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 9 of its 43 references that are in the map. First posted on arXiv 29 Feb 2024.

Builds on

9 of 43 references.

References · 9

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,029
  3. 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 773
  4. 2022PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Changecited by 372
  5. 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 308
  6. 2022Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning taskscited by 56
  7. 2023Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMscited by 45
  8. 2023Learning adaptive planning representations with natural language guidancecited by 36
  9. 2023Automating the Generation of Prompts for LLM-based Action Choice in PDDL Planningcited by 24

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 6; 2026: 6.

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  1. 2025Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlocited by 45
  2. 2025LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Modelscited by 38
  3. 2025Large Language Models for Planning: A Comprehensive and Systematic Surveycited by 38
  4. 2024On the Limit of Language Models as Planning Formalizerscited by 17
  5. 2024PDDLEGO: Iterative Planning in Textual Environmentscited by 14
  6. 2024CaT-BENCH: Benchmarking Language Model Understanding of Causal and Temporal Dependencies in Planscited by 10
  7. 2025Unifying Inference-Time Planning Language Generationcited by 3
  8. 2025Language Model as Planner and Formalizer under Constraintscited by 3
  9. 2026Theory-Scale Auto-Formalization of Logics for Computer Sciencecited by 3
  10. 2026Language Models as Higher-Order Planning Formalizerscited by 1
  11. 2025GinSign: Grounding Natural Language Into System Signatures for Temporal Logic Translationcited by 0
  12. 2026Specification-Driven Generation and Evaluation of Discrete-Event World Models via the DEVS Formalismcited by 0
3 more
  1. 2026Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agentscited by 0
  2. 2026A Neurosymbolic Approach for Constructing Planning Domain Models from Clinical Narrativescited by 0
  3. 2026From Solver Feedback to Faithful Plans: Multi-Role Reinforcement Learning for Symbolic Planningcited by 0