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We present an Interactive Evolutionary Computation (IEC) system that applies user gaze information. Historically, IEC systems have encountered the problems caused by heavy user evaluation loads. To solve these problems, researchers have employed biologically derived user information, such as heartbeats or brainwaves, to reduce the evaluation load. However, the requirement for users to wear special devices to measure this information has limited the popularity of these systems. Therefore, we applied the user gaze information approach to solve these problems. Gaze information includes the user's potential preferences, which are derived from various processes. When user gaze information is applied in the evaluation of candidate solutions, IEC systems can obtain user evaluation information while users are viewing multiple candidate solutions. In this paper, we verify the effectiveness of the eye tracking IEC system using evaluation experiments with real users. In the experiment, we use a normal IEC system as a comparison method where users manually evaluate candidate solutions using a 10-stage evaluation process. The experimental results show that the eye tracking IEC method can generate solutions with results equivalent to those of the compared system.

Several studies on how social robots respond, gesture, and display emotion in human-robot interactions have been conducted. In particular, sociality of robots implies that robots do not only exhibit human-like behaviors, but also display a tendency to adapt to a group of individuals. For robots to exhibit sociality, they need to adapt to group norms without telling them how to behave by the group members. In this study, we investigated the effect of group norms on human decision-making in human-robot groups, which comprise two robots using our proposed robotic model. Furthermore, we conducted quizzes with the robots and a human participant using unclear and vague answers. We assessed this influence by making the participant and the two robots repeat a set of actions: to answer the same quiz and recognize each answer of the group members. Additionally, we evaluated the extent to which the group norms changed the opinions of humans using a questionnaire. We analyzed the results of the questionnaire and chronological change in their answers for the quiz with the same question. The quiz experimental results showed that the human participants changed their answers after they discovered the answers of the robots for the first time due to social influence from the robots assumed that the human participants were confused about the diversity of the answers in the group and were aware of the consideration of the robots of the group norm. This is to ensure that they can adjust their answers to the group norm. Moreover, the questionnaire results revealed that the group norms gave the human participants right answers to the quiz that has no correct answers. Therefore, we concluded that robots attempt to comply with a group norm affects human's decision-making. INDEX TERMS Social robotics, human-robot interaction, human-robot group, social influence, group norm.

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