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Explainable artificial intelligence is a research field that tries to providemore transparency for autonomous intelligent systems. Explainability has beenused, particularly in reinforcement learning and robotic scenarios, to betterunderstand the robot decision-making process. Previous work, however, has beenwidely focused on providing technical explanations that can be betterunderstood by AI practitioners than non-expert end-users. In this work, we makeuse of human-like explanations built from the probability of success tocomplete the goal that an autonomous robot shows after performing an action.These explanations are intended to be understood by people who have no or verylittle experience with artificial intelligence methods. This paper presents auser trial to study whether these explanations that focus on the probability anaction has of succeeding in its goal constitute a suitable explanation fornon-expert end-users. The results obtained show that non-expert participantsrate robot explanations that focus on the probability of success higher andwith less variance than technical explanations generated from Q-values, andalso favor counterfactual explanations over standalone explanations.

Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios