Bridging the Human-Agent Representation Gap for Decision-Making Explanations in Autonomous Robots

In autonomous vehicle mission planning, supporting human operators to understand and influence the decision-making process is crucial for building the operator’s trust and establishing effective collaboration. However, it has been observed that human and agent representations will typically not align. As a consequence, concepts that are useful for effective human-agent communication, will not necessarily feature in the agent’s representation. Focusing on specific spatial-temporal concepts, we define automatic model extensions, which can introduce these additional concepts. We report on a qualitative user study, where we investigate the use of these new structural concepts in underwater autonomous vehicle scenarios. Our study indicates that the extended concepts can be used in user queries and agent responses, enabling the user to better communicate their intent in shaping mission objectives, and supporting explanations with more relevant information.