Analysing Explanation-Related Interactions in Collaborative Perception-Cognition-Communication-Action
This work addresses the need for AI-equipped robots to provide effective explanations to cooperate with humans in collaborative settings, though it is incremental as it focuses on analysis rather than developing new explanation methods.
The paper tackled the problem of understanding what types of explanations humans expect from teammates in collaborative emergency response tasks by analyzing human communications, finding that most explanation-related messages seek clarification in decisions or actions and that these messages impact task performance.
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.