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Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning
arXiv – CS AI|Guilhem Fouilh\'e, Rebecca Eifler, Antonin Poch\'e, Sylvie Thi\'ebaux, Nicholas Asher||4 views
🤖AI Summary
Researchers present a multi-agent Large Language Model framework for interactive AI planning systems that provides context-dependent explanations to human planners. The system aims to facilitate collaborative decision-making between humans and AI rather than replacing human planners entirely.
Key Takeaways
- →New LLM architecture enables natural interaction between humans and AI planning systems through conversational explanations.
- →The framework is designed to be agnostic to explanation methods and adaptable to different user contexts.
- →Research focuses on augmenting human decision-making rather than replacing human planners in sequential decision problems.
- →User study demonstrates the effectiveness of LLM-powered interactions compared to template-based explanation interfaces.
- →Goal-conflict explanations are used as a specific instantiation of the broader interactive explanation framework.
#llm#ai-planning#human-ai-collaboration#interactive-explanations#multi-agent#decision-making#conversational-ai#research
Read Original →via arXiv – CS AI
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