为机器人在厨房清洁时的异常行为设计智能解释机制
Effective Explanations for Belief-Desire-Intention Robots: When and What to Explain
- 根据情境突变判断是否需要解释，避免无效打扰
- 用户偏好简洁说明动作意图与关键环境因素
- 可嵌入BDI机器人系统，支持个性化解释
当机器人在日常生活中执行复杂且依赖上下文的任务时，行为偏离预期容易让用户困惑。提供机器人推理过程的解释有助于用户理解其意图。然而，何时提供解释以及解释内容的设计至关重要，否则可能引发用户反感。本文研究了用户对协助厨房清洁任务的机器人解释需求与内容偏好的实验结果表明，用户仅在遭遇意外情境时希望获得解释，且更倾向简洁明了的说明——明确指出令人困惑行为背后的意图及相关的上下文因素。基于此发现，本文提出两种算法：一是识别意外行为的触发条件，二是生成有效解释的内容构造方法。这两项算法可无缝集成至信念-欲望-意图（Belief-Desire-Intention, BDI）机器人推理流程中，为实现基于上下文和用户个性化的高效人机交互铺平道路。
原文摘要 · Abstract (English)
When robots perform complex and context-dependent tasks in our daily lives, deviations from expectations can confuse users. Explanations of the robot's reasoning process can help users to understand the robot intentions. However, when to provide explanations and what they contain are important to avoid user annoyance. We have investigated user preferences for explanation demand and content for a robot that helps with daily cleaning tasks in a kitchen. Our results show that users want explanations in surprising situations and prefer concise explanations that clearly state the intention behind the confusing action and the contextual factors that were relevant to this decision. Based on these findings, we propose two algorithms to identify surprising actions and to construct effective explanations for Belief-Desire-Intention (BDI) robots. Our algorithms can be easily integrated in the BDI reasoning process and pave the way for better human-robot interaction with context- and user-specific explanations.
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