让大模型通过对话探索规划空间，实现可交互的智能解释。
Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning
- 设计多智能体架构，支持根据用户提问动态生成解释。
- 用户研究显示，大模型交互比模板式解释更易理解。
- 适合需要人机协作规划的复杂决策场景。
在自动化现实世界序列决策问题的规划生成中，目标通常不是取代人类规划者，而是促进一种迭代推理与信息获取过程，由人类依据自身偏好和专业知识引导AI规划器。在此背景下，针对用户问题生成解释对于提升其对潜在解决方案的理解、增强对系统的信任至关重要。为此，我们提出一种不依赖特定解释框架的多智能体大语言模型架构，支持用户与上下文相关的交互式解释。我们还实现了目标冲突解释的实例化，并通过用户研究对比了基于大模型的交互界面与基线模板式解释界面的表现。
原文摘要 · Abstract (English)
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.
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