Model Space Reasoning as Search in Feedback Space for Planning Domain Generation

1. 1Define a planning domain generation problem using natural language descriptions
2. 2Implement an agentic language model feedback framework to generate planning domains
3. 3Search in feedback space to refine the generated planning domains
4. 4Evaluate the quality of the generated planning domains using metrics such as accuracy and completeness
5. 5Refine the model space reasoning approach based on the evaluation results

Researchers and developers in AI planning and natural language processing can benefit from this approach to generate high-quality planning domains, which can be deployed in practice

💡 Model space reasoning as search in feedback space can improve the generation of planning domains from natural language descriptions

🤖 Generate planning domains using model space reasoning as search in feedback space! 🚀 Improving planning domain generation with agentic language models #AIplanning #NLP