Effective Explanations for Belief-Desire-Intention Robots: When and What to Explain
This addresses user understanding and annoyance in human-robot interaction for daily tasks, but it is incremental as it builds on existing BDI frameworks.
The paper tackled the problem of when and what to explain for belief-desire-intention robots to avoid user confusion, finding that users prefer explanations in surprising situations with concise content about intentions and contextual factors.
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.