[2603.02070] Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning

Computer Science > Artificial Intelligence arXiv:2603.02070 (cs) [Submitted on 2 Mar 2026] Title:Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning Authors:Guilhem Fouilhé, Rebecca Eifler, Antonin Poché, Sylvie Thiébaux, Nicholas Asher View a PDF of the paper titled Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning, by Guilhem Fouilh\'e and Rebecca Eifler and Antonin Poch\'e and Sylvie Thi\'ebaux and Nicholas Asher View PDF HTML (experimental) Abstract: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. To enable natural interaction with such a system, we present a multi-agent Large Language Model (LLM) architecture that is agnostic to the explanation framework and enables user- and context-dependent interactive explanations. We also describe an instantiation of this framework for goal-conflict explanations, which we use to conduct a user study comparing the LLM-powered interaction with a baseline template-based explanation interface. Comments: Subjects: Artifici...