Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models
📰 ArXiv cs.AI
Researchers explore using Large Language Models to assist classical planners in solving large-scale planning problems
Action Steps
- Investigate the use of LLMs to generate helpful actions and states to prune the search space
- Explore the integration of LLMs with domain-specific knowledge to enhance planning capabilities
- Evaluate the performance of LLM-assisted planners in solving large-scale planning problems
- Analyze the trade-offs between using LLMs for inspiration versus prediction in planning tasks
Who Needs to Know This
AI researchers and software engineers working on planning and decision-making systems can benefit from this research, as it has the potential to improve the efficiency and effectiveness of their systems
Key Insight
💡 LLMs can be used to generate helpful actions and states to prune the search space, improving planning efficiency
Share This
💡 LLMs can help classical planners solve large-scale problems!
Key Takeaways
Researchers explore using Large Language Models to assist classical planners in solving large-scale planning problems
Full Article
Title: Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models
Abstract:
arXiv:2508.11524v2 Announce Type: replace Abstract: Addressing large-scale planning problems has become one of the central challenges in the planning community, deriving from the state-space explosion caused by growing objects and actions. Recently, researchers have explored the effectiveness of leveraging Large Language Models (LLMs) to generate helpful actions and states to prune the search space. However, prior works have largely overlooked integrating LLMs with domain-specific knowledge to e
Abstract:
arXiv:2508.11524v2 Announce Type: replace Abstract: Addressing large-scale planning problems has become one of the central challenges in the planning community, deriving from the state-space explosion caused by growing objects and actions. Recently, researchers have explored the effectiveness of leveraging Large Language Models (LLMs) to generate helpful actions and states to prune the search space. However, prior works have largely overlooked integrating LLMs with domain-specific knowledge to e
DeepCamp AI