Abstract
Current approaches to implement eXplainable Autonomous Robots (XAR) are dominantly based on Reinforcement Learning (RL), which are suitable for modelling and correcting people’s first-order mental state attributions to robots. Our recent findings show that people also rely on attributing second-order beliefs (i.e., beliefs about beliefs) to robots to interpret their
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