This research develops a conversational robot that stimulates users’ dialogue satisfaction and motivation in non-task-oriented dialogues that include opinion and/or preference exchanges. One way to improve user satisfaction and motivation is by demonstrating the robot’s ability to understand user opinions. In this paper, we explore a method that efficiently obtains the concept of user preferences: likes and dislikes. The concept is acquired by complementing a small amount of user preference data observed in dialogues. As a method for efficient collection, we propose a dialogue strategy that creates utterances with the largest expected complementation. Our experimental results with a female-type android robot suggest that the proposed strategy efficiently obtained user preferences and enhanced dialogue satisfaction. In addition, the strength of user motivation (i.e., long-term willingness to communicate with the android) is only positively correlated with the android’s willingness to understand. Our results not only show the effectiveness of our proposed strategy but also suggest a design theory for dialogue robots to stimulate dialogue motivation, although the current results are derived only from a female-type android.

这项研究开发了一种对话型机器人，该机器人可以在非任务导向的对话（包括观点和/或偏好交换）中刺激用户的对话满意度和动机。提高用户满意度和动力的一种方法是展示机器人理解用户意见的能力。在本文中，我们探索了一种有效获得用户偏好概念的方法：喜欢和不喜欢。通过补充在对话中观察到的少量用户偏好数据来获得该概念。作为有效收集的一种方法，我们提出了一种对话策略，该对话策略可产生具有最大预期补语的语音。我们用女性型Android机器人进行的实验结果表明，提出的策略有效地获得了用户的偏好并提高了对话满意度。此外，用户动机的强度（即，长期与android通信的意愿）与android的理解意愿正相关。我们的结果不仅显示了我们提出的策略的有效性，而且还提出了对话机器人刺激对话动机的设计理论，尽管当前结果仅来自女性型机器人。