Many social robots have emerged in public places to serve people. For these services, the robots are assumed to be able to present internal aspects (i.e., mind, sociability) to engage and interact with people over the long term. In this paper, we propose a novel dialogue structure called experience-based dialogue to help a robot present and maintain a good interaction over the long term. This dialogue structure contains a piece of knowledge and a story about how the robot gained this knowledge, which are used to compose the robot’s experience-related utterances for sharing experiences of interacting with previous users other than just the current user and help it present its internal aspects. We conducted an experiment to test the effects of our proposed dialogue structure and measure them with some published subjective scales. The results showed that experience-based dialogue can help a robot obtain better evaluations in terms of perceived intelligence, sociability, mind, anthropomorphism, animacy, likability, level of acceptance, and positive user reaction.

在公共场所出现了许多社交机器人来为人们服务。对于这些服务，假定机器人能够展现内部方面（即思维，社交能力），以便长期与人互动。在本文中，我们提出了一种新颖的对话结构，称为“基于经验的对话”，以帮助机器人长期呈现并保持良好的交互性。该对话结构包含一段知识和一个有关机器人如何获得此知识的故事，这些知识和故事用于构成机器人的体验相关言论，以便与当前用户（而非当前用户）共享与以前的用户进行交互的经验，并帮助其展示其内部方面。我们进行了一项实验，以测试我们提议的对话结构的效果，并使用一些已发布的主观量表对其进行测量。