Generating Explanations for Autonomous Robots Using Assumption-Alignment Tracking

As the techniques of autonomous robots advance, there is an increasing demand for robots to provide explanations for their behavior. There are two commonly used explanation types. The first type emphasizes that a robot's policy is the best (or only) option that satisfies a specific property produced by its decision-making algorithms. The second explanation type is used when a robot fails and describes the cause of an error state that led to the failure. This paper proposes a new explanation type derived from a robot's proficiency self-assessment. The proposed explanation type not only supplements the first explanation type under typical operating conditions but also includes the second explanation type when the robot fails. The proposed explanation type is based on assumption-alignment tracking (AAT), a novel method for robot proficiency self-assessment. AAT provides three pieces of information for explanation generation: (1) assessment of assumptions veracity on which the robot's generators rely; (2) proficiency assessment measured by the probability that the robot will successfully accomplish its task; (3) counterfactual proficiency assessment computed by hypothetically varying assumptions. The information provided by AAT fits the situation awareness-based framework for explainable artificial intelligence. Examples of generated explanations are demonstrated using a simulated robot setting up a table with different blocks.