Model Space Reasoning as Search in Feedback Space for Planning Domain Generation
Summary: arXiv:2604.08712v1 Announce Type: new
Abstract
The generation of planning domains from natural language descriptions remains an open problem even with the advent of large language models (LLMs) and reasoning models. Recent work suggests that while LLMs have the ability to assist with domain generation, they are still far from producing high-quality domains that can be deployed in practice.
Introduction
In the context of artificial intelligence, planning domain generation is crucial for enabling intelligent agents to understand and operate within various environments. Despite notable advancements in LLMs, the translation of natural language into structured planning domains poses significant challenges. This article explores a novel approach utilizing an agentic language model feedback framework to enhance the effectiveness of planning domain generation.
Challenges in Planning Domain Generation
Generating planning domains from natural language descriptions involves several complexities:
- Ambiguity in Language: Natural language is often ambiguous, making it difficult for models to derive clear and actionable planning domains.
- Lack of Context: Without sufficient contextual understanding, LLMs struggle to generate relevant and precise domain specifications.
- Quality Assurance: Ensuring the generated domains meet practical deployment standards is a significant hurdle.
Methodology
This research investigates the efficacy of an agentic language model feedback framework, which incorporates a minimal amount of symbolic information to enhance domain generation. The framework leverages various forms of symbolic feedback, including:
- Landmarks: Key reference points that assist in defining the structure and components of the planning domain.
- VAL Plan Validator: A tool that checks the validity of generated plans and provides constructive feedback on their feasibility.
Heuristic Search Over Model Space
Utilizing heuristic search over model space plays a pivotal role in optimizing domain quality. This process allows for the iterative improvement of planning domains based on feedback received from symbolic validators. By systematically exploring various model configurations, the framework aims to discover optimal solutions that enhance the overall quality of the generated domains.
Results and Evaluation
Preliminary results indicate that the integration of symbolic feedback significantly improves the quality of generated planning domains. Domains created with the assistance of landmarks and validation feedback exhibited:
- Increased accuracy in reflecting the original natural language description.
- Higher feasibility rates when assessed by the VAL plan validator.
- Enhanced clarity and structure, making them more suitable for practical application.
Conclusion
The study demonstrates the potential of combining LLMs with symbolic feedback mechanisms to improve planning domain generation from natural language. By employing heuristic search techniques, the framework not only addresses existing challenges but also paves the way for future advancements in AI planning systems.
Future Work
Further research is essential to refine this approach, exploring additional forms of feedback and expanding the framework’s capabilities to handle more complex planning scenarios.