研究人机协作中解释性沟通，发现决策澄清最需解释能力。
Analysing Explanation-Related Interactions in Collaborative Perception-Cognition-Communication-Action
- 分析模拟应急任务中人类互动的解释类信息
- 多数解释需求聚焦于对行为决策的澄清
- 解释类消息显著提升协同任务表现
在协同任务中，有效沟通对合作至关重要。配备AI的机器人需能解释自身行为，以实现高效协作并赢得信任。本文分析了人类参与者在模拟应急响应任务中的交互沟通，识别出与可解释AI文献中多种互动解释类型相对应的信息。结果表明，大多数解释类消息旨在澄清行为或决策，且这些消息显著影响任务完成效果。该研究揭示了人类在紧急协作场景下对队友解释的期待，明确了机器人最需具备的解释能力方向。
原文摘要 · Abstract (English)
Effective communication is essential in collaborative tasks, so AI-equipped robots working alongside humans need to be able to explain their behaviour in order to cooperate effectively and earn trust. We analyse and classify communications among human participants collaborating to complete a simulated emergency response task. The analysis identifies messages that relate to various kinds of interactive explanations identified in the explainable AI literature. This allows us to understand what type of explanations humans expect from their teammates in such settings, and thus where AI-equipped robots most need explanation capabilities. We find that most explanation-related messages seek clarification in the decisions or actions taken. We also confirm that messages have an impact on the performance of our simulated task.
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