Emotional expressions serve as important communicative tools in human negotiations, and prior work has shown that artificial agents can use synthetic expressions to enhance negotiation outcomes and to train negotiation skills. These prior findings have focused on virtual agents and little is known about the effect of expressions when negotiating with physical robots. Therefore, in this study, we compared how participants negotiated with emotionally expressive virtual agents and android robots. Participants$(\mathrm{n}={82})$, as a proposer, played a nonverbal version of a four-issue ultimatum bargaining game with a counterpart who was either a virtual agent or an android robot. Before negotiating, participants observed their counterpart's emotional reactions to potential deals. The results showed that participants were better able to estimate the preferences of virtual counterparts compared with robotic counterpart, and thereby achieve better win-win solutions. We find this effect was mediated by uncanniness: participants found the emotional robot to be uncanny, and this undermined their ability to extract information from the robot's expressions. We discuss theoretical mplications for our understanding of human-robot negotiation and practical implications for the design of effective robot negotiators.

With the age of diversification, it’s important to nurture the qualities of children to enable them to create their future society. To achieve this, it’s crucial for children to broaden and deepen their views through dialogue with others. In the field of human-agent interaction, introducing robots into educational settings has shown positive educational effects. In this study, we developed an autonomous dialogue robot that delved into the children’s ideas and prompted them to think about aspects they had not yet considered, to broaden and deepen their viewpoints. We introduced this developed robot into a 6th-grade science class and conducted an experiment. The results based on subjective evaluations by students through questionnaires and objective evaluations of the students’ reaction papers by third parties, suggest that discussions with the robot can broaden and deepen the viewpoints of students. This study highlights the potential of robots to support the important educational goal.

This research attempts to develop a conversational robot that motivates users to interact with it for long-term non-task-oriented dialogue. In human-human interactions, the exchange of subjective opinions is important, but in human-robot interactions, it has been reported that the users’ willingness to talk decreases when they cannot attribute subjective opinions to robots. Accordingly, this study investigates what factors are involved in the attribution of subjective opinions to a conversational android robot. The experimental results identified three factors of the participants’ recognition involved in attributing subjective opinions to a conversational android: their own abilities, the android’s five senses, and their own emotionality.

The goal of this study is to develop an autonomous conversational robot that acquires knowledge about human society in the real world. In this study, we define experience data as image data obtained from a camera (visual information) and dialogue data obtained from a microphone (auditory information). From experience data, the robot acquires knowledge about the co-occurrence between humans and items, that is, whether it is usual or not for humans to have the items. Not only does the proposed system acquire knowledge, but also generates utterances based on acquired knowledge. We have implemented the proposed method on an android, a human-like robot. We conducted an experiment in which the android was placed in a real-world environment (shopping mall) and interacted with visitors. The percentage of positive responses to the robot's questions based on the acquired knowledge suggests that this system can acquire knowledge from experience data.

The following topics are dealt with: human-robot interaction; mobile robots; learning (artificial intelligence); control engineering computing; humanoid robots; service robots; medical robotics; robot vision; computer aided instruction; motion control.

Our research goal was to develop a robot counseling system. It is important for a counselor to promote self-disclosure of clients to reduce their anxiety feelings. However, when a counselor is human, clients sometimes hesitate to disclose intrusive topics due to embarrassment and self-esteem issues. We hypothesized that a robot counselor, on account of its unique kind of agency, could remove mental barriers between the counselor and the client, and promote in-depth self-disclosure about negative topics. In this study, we prepared two robots (an android and a desktop robot) as robot counselors. First, we confirmed that subjects eagerly self-disclosed to these prepared robots from the numbers of spoken words about self-disclosure in preliminary experiment. And next, we conducted the experiment to verify whether it is possible to expose more of subjects' weakness to robots than humans. The experimental result suggested that robots can draw out subjects' self-disclosure about negative topics than the human counselor.

This work studies a dialogue strategy aimed at building people's motivation for talking with autonomous conversational robots. Even though spoken dialogue systems continue to develop rapidly, the existing systems are insufficient for continuous use because they fail to motivate users to talk with them. One reason is that users fail to realize that the intentions of the system's utterances are based on its values. Since people recognize the values of others and modify their own values in human-human conversations, we hypothesize that a dialogue strategy that makes users saliently feel the difference of their own values and those of the system will increase motivation for the dialogues. Our experiment, which evaluated human-human dialogues, supported our hypothesis. However, an experiment with human-android dialogues failed to produce identical results, suggesting that people did not attribute values to our android. For a conversational robot, we need additional techniques to convince people to believe a robot speaks based on its own values and opinions.

The goal of this study is to develop a humanoid robot that can conduct a continuous conversation with people. Spoken dialogue systems have recently been rapidly developed, but the existing systems are insufficient for continuous use because they fail to inspire the user's motivation to talk with them. This is because a user is unable to feel that a robot possesses its own intentions, and thus the robot must acquire its own values to convey its sense of intentionality to users through its spoken communications. This paper focuses on a dialogue strategy aimed at building people's motivation under the assumption that the robot has a values-based dialogue system. People's motivation can be influenced by the intentionality as well as by the affinity of the robot. We hypothesized that there is a good disagreement/agreement ratio in a conversation to efficiently balance people's feelings of intentionality and affinity. The result of a psychological experiment using an android robot partially supported our hypothesis.