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Explainable AI (XAI) is a subfield of human-agent interaction that involves the design and development of methods that generate explanations and exhibit more transparent behavior from AI agents. In this work we present three contributions that advance XAI research in the context of Human-Robot Interaction (HRI). First, we extend explanation generation using behavior trees to include projection-level XAI, i.e. the ability to query an agent for explanations on future actions. Second, we developed algorithms that answer pre- and post-conditions of an action, which we hypothesize improves comprehension of an agent. Third, we present an experimental design using a robot arm and GUI to evaluate the efficacy of the explanation generation approach on various levels of user situational awareness, workload, and trust. All code is open-source to allow researchers to explore using explanation generation with behavior trees for future human-robot interaction studies.
Barkouki et al. (Mon,) studied this question.