Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning

ArXi:2603.02070v2 Announce Type: replace-cross When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facilitate an iterative reasoning and elicitation process, where the human's role is to guide the AI planner according to their preferences and expertise. In this context, explanations that respond to users' questions are crucial to improve their understanding of potential solutions and increase their trust in the system.