Disclosing personal matters to other individuals often contributes to the maintenance of our mental health and social bonding. However, in face-to-face situations, it can be difficult to prompt others to self-disclose because people often feel embarrassed disclosing personal matters to others. Although artificial agents without strong social pressure for listeners to induce self-disclosure is a promising engineering method that can be applied in daily stress management and reduce depression, gender difference is known to make a drastic difference of the attitude toward robots. We hypothesized that, as compared to men, women tend to prefer robots as a listener for their self-disclosure. The experimental results that are based on questionnaires and the actual self-disclosure behavior indicate that men preferred to self-disclose to the human listener, while women did not discriminate between robots and humans as listeners for their self-disclosure in the willingness and the amount of self-disclosure. This also suggests that the gender difference needs to be considered when robots are used as a self-disclosure listener.

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.

We propose a strategy with which conversational android robots can handle dialogue breakdowns. For smooth human-robot conversations, we must not only improve a robot's dialogue capability but also elicit cooperative intentions from users for avoiding and recovering from dialogue breakdowns. A cooperative intention can be encouraged if users recognize their own responsibility for breakdowns. If the robot always blames users, however, they will quickly become less cooperative and lose their motivation to continue a discussion. This paper hypothesizes that for smooth dialogues, the robot and the users must share the responsibility based on psychological reciprocity. In other words, the robot should alternately attribute the responsibility to itself and to the users. We proposed a dialogue strategy for recovering from dialogue breakdowns based on the hypothesis and experimentally verified it with an android. The experimental result shows that the proposed method made the participants aware of their share of the responsibility of the dialogue breakdowns without reducing their motivation, even though the number of dialogue breakdowns was not statistically reduced compared with a control condition. This suggests that the proposed method effectively elicited cooperative intentions from users during dialogues.

This study employs Conversation Analysis to create a recursive model that improves the quality of human-robot interaction. Our research goal is to create a dialogue robot that offers pleasant experiences for users, so they are willing to engage in repeated interactions in daily lives. While there has been dramatic progress in the performance of dialogue robots, there has been less attention to the importance of users’ interactional experience compared to the “specs” of the dialogue system. Employing Goffmanian insights and using research in Conversation Analysis (CA), the present study develops a dialogue closing system to exit the interaction. We then experimentally verified that the robot with the dialogue closing system performs better in the user’s perception of the robot (i.e. likeability, politeness, and dialogue satisfaction) than the control group. Further, by analyzing the dialogue between the human and the robot through CA, we propose to build a recursive, reflective model to improve the dialogue model design. A constructive approach urges us to reproduce complicated social phenomena in human-robot interaction so that we can investigate the underlying cognitive mechanisms of humans and create robots that can convey human-like cognition functions and coexist with humans. Taking such a constructive approach, we posit that our recursive model for dialogue systems that uses CA insights and then qualitatively analyzes conversational data can enhance the quality of dialogue systems because the model elucidates which properties of a conversation humans need to experience a conversational robot as human-like. Our study suggests that interactional morality - particularly conversational closings - is one property of human interactions that humans likely require social robots to adhere to.

Personalizing a dialogue system according to the user has been recognized to have various positive effects. Despite the significance of user values, concepts guiding choices and evaluations being recognized in communication, they have not been considered in personalized dialogue systems. Therefore, this study constructs a dialogue system that understands user values through conversation. Furthermore, the impact of understanding values on the interactions between dialogue systems and users is examined. The method is organized with a user model of preferences and values based on the established means-end chain model. We used a large language model (LLM) to estimate values based on the users’ preferences and the reasons they prefer them. Furthermore, an infinite relational model (IRM) estimates the relationships between multiple elements within the user model. The experiments show that the proposed method could estimate user values and enhance animacy and perceived intelligence in users’ impressions of an android robot, prompting new insights into users’ own values. The perception of the robot contributes to improved-quality interactions, and new insights into values facilitate a deeper self-understanding of users. This achievement, demonstrating the effects of using values for interaction, can provide valuable insights into human-robot interaction.

In recent years, the development of robots that can engage in non-task-oriented dialogue with people, such as chat, has received increasing attention. This study aims to clarify the factors that improve the user’s willingness to talk with robots in non-task oriented dialogues (e.g., chat). A previous study reported that exchanging subjective opinions makes such dialogue enjoyable and enthusiastic. In some cases, however, the robot’s subjective opinions are not realistic, i.e., the user believes the robot does not have opinions, thus we cannot attribute the opinion to the robot. For example, if a robot says that alcohol tastes good, it may be difficult to imagine the robot having such an opinion. In this case, the user’s motivation to exchange opinions may decrease. In this study, we hypothesize that regardless of the type of robot, opinion attribution affects the user’s motivation to exchange opinions with humanoid robots. We examined the effect by preparing various opinions of two kinds of humanoid robots. The experimental result suggests that not only the users’ interest in the topic but also the attribution of the subjective opinions to them influence their motivation to exchange opinions. Another analysis revealed that the android significantly increased the motivation when they are interested in the topic and do not attribute opinions, while the small robot significantly increased it when not interested and attributed opinions. In situations where there are opinions that cannot be attributed to humanoid robots, the result that androids are more motivating when users have the interests even if opinions are not attributed can indicate the usefulness of androids.