Analysing Explanation-Related Interactions in Collaborative Perception-Cognition-Communication-Action

TL;DR

It is found that most explanation-related messages seek clarification in the decisions or actions taken in the decisions or actions taken in a simulated emergency response task.

Abstract

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.

Figures (3)

• Figure 2: A top view and first-person view of the simulation environment TeamCollab fusion. Agents only have access to their avatar's first-person view.
• Figure 3: Different evaluations of performance by the number of team messages sent.
• Figure 4: Majority Label Distribution ($\geq 3$ agreement level)