An agentic LLM framework augmented with symbolic feedback and heuristic search over model space generates improved planning domains from natural language descriptions.
Leveraging pre- trained large language models to construct and utilize world models for model-based task plan- ning
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Model Space Reasoning as Search in Feedback Space for Planning Domain Generation
An agentic LLM framework augmented with symbolic feedback and heuristic search over model space generates improved planning domains from natural language descriptions.