Autonomous Policy Explanations for Effective Human-Machine Teaming

Abstract Policy explanation, a process for describing the behavior of an autonomous system, plays a crucial role in effectively conveying an agent's decision-making rationale to human collaborators and is essential for safe real-world deployments. It becomes even more critical in effective human-robot teaming, where good communication allows teams to adapt and improvise successfully during uncertain situations by enabling value alignment within the teams. This thesis proposal focuses on improving human-machine teaming by developing novel human-centered explainable AI (xAI) techniques that empower autonomous agents to communicate their capabilities and limitations via multiple modalities, teach and influence human teammates' behavior as decision-support systems, and effectively build and manage trust in HRI systems.