Explainable artificial intelligence is a research field that tries to provide more transparency for autonomous intelligent systems. Explainability has been used, particularly in reinforcement learning and robotic scenarios, to better understand the robot decision-making process. Previous work, however, has been widely focused on providing technical explanations that can be better understood by AI practitioners than non-expert end-users. In this work, we make use of human-like explanations built from the probability of success to complete the goal that an autonomous robot shows after performing an action. These explanations are intended to be understood by people who have no or very little experience with artificial intelligence methods. This paper presents a user trial to study whether these explanations that focus on the probability an action has of succeeding in its goal constitute a suitable explanation for non-expert end-users. The results obtained show that non-expert participants rate robot explanations that focus on the probability of success higher and with less variance than technical explanations generated from Q-values, and also favor counterfactual explanations over standalone explanations.
翻译:可解释的人工智能是一个研究领域,它试图为自主智能系统提供更大的透明度。解释性已被使用,特别是在强化学习和机器人假设中,以更好地了解机器人的决策过程。然而,以前的工作广泛侧重于提供技术解释,使AI从业人员比非专家终端用户更能理解这些技术解释。在这项工作中,我们利用从成功概率中得出的类似人类的解释来完成自主机器人在采取行动后显示的目标。这些解释意在为那些在人工智能方法方面没有经验或经验极少的人所理解。本文件将用用户的试验来研究这些解释是否侧重于一项行动在目标上取得成功的可能性,这是对非专家终端用户的适当解释。获得的结果表明,非专家参与者对机器人解释的评级侧重于成功概率,与从“价值”中得出的技术解释相比,其成功概率更高,差异较小,也有利于反证解释而不是独立解释。