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.

翻译：在真实世界的序贯决策问题中，自动化规划生成的目标通常并非取代人类规划者，而是促进一种迭代推理与需求挖掘过程——人类在此过程中的角色是根据自身偏好与专业知识引导AI规划者。在此背景下，能够响应用户疑问的解释对于提升其对潜在方案的理解、增强对系统的信任至关重要。为实现与此类系统的自然交互，我们提出了一种多智能体大语言模型架构，该架构独立于具体解释框架，支持用户和上下文相关的交互式解释。我们同时描述了该框架在目标冲突解释场景下的具体实现，并以此进行了一项用户研究，将基于大语言模型的交互方式与基于模板的基线解释界面进行了比较。