ABSTRACT

To realize autonomous collaborative robots, it is important to increase the trust that users have in them. Toward this goal, this paper proposes an algorithm that endows an autonomous agent with the ability to explain the transition from the current state to the target state in a Markov decision process (MDP). According to cognitive science, to generate an explanation that is acceptable to humans, it is important to present the minimum information necessary to sufficiently understand an event. To meet this requirement, we propose a framework for identifying important elements in the decision-making process using a prediction model for the world and generating explanations based on these elements. To verify the ability of the proposed method, we conducted an experiment using a grid environment. It was inferred from the result of a simulation experiment that the explanation generated using the proposed method was composed of the minimum elements important for understanding the transition from the current state to the target state. Furthermore, subject experiments showed that the generated explanation was a good summary of the process of state transition, and that a high evaluation was obtained for the explanation of the reason for an action.

摘要

要实现自主协作机器人，重要的是增加用户对它们的信任。为实现这一目标，本文提出了一种算法，该算法赋予自治代理解释马尔可夫决策过程 (MDP) 中从当前状态到目标状态的转换的能力。根据认知科学，要产生人类可接受的解释，重要的是提供充分理解事件所需的最少信息。为了满足这一要求，我们提出了一个框架，用于使用世界预测模型识别决策过程中的重要元素，并基于这些元素生成解释。为了验证所提出方法的能力，我们使用网格环境进行了实验。从模拟实验的结果推断，使用所提出的方法生成的解释由对于理解从当前状态到目标状态的转变很重要的最少元素组成。此外，主题实验表明，生成的解释很好地总结了状态转换的过程，并且对动作原因的解释得到了很高的评价。