The challenges of first-and second-order belief reasoning in explainable human-robot interaction

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 behavior. However, robots arguably form and act primarily on first-order beliefs and desires (about things in the environment) and do not have a functional “theory of mind”. Moreover, RL models may be incapable to appropriately address second-order belief attribution errors. This paper aims to open a discussion of what our recent findings on second-order mental state attribution to robots imply for current approaches to XAR.