Oka Natsuki

Dynamic decision support systems in a multi-agent environment

Multi-agent systems are widely used in modeling and control of modern industrial systems in such areas as transportation, supply chain management, simulations, and fault detection. These systems are often used for tasks where the environment is complex and constantly changing and thus requires a high degree of flexibility. In this paper, we propose a method to increase the flexibility and security of multi-agent systems by using dynamic decision support systems for intelligent agents. We demonstrate that this method significantly improves the flexibility and security of multi-agent systems by simplifying run-time modifications to the system.

Does Talking to a Robot in a High-Pitched Voice Create a Good Impression of the Robot?

When talking to infants, we tend to use infant-directed speech (IDS) rather than adult-directed speech (ADS). IDS attracts more attention from infants, conveys the emotions of adults more easily, and makes language acquisition easier. It is not clear, however, whether IDS has a cognitive effect on adults as well. To address this issue, we focus on one of the most distinctive features of IDS, a high-pitched voice. In addition, we conduct two human-robot interaction experiments to examine the following two hypotheses: (i) a robot reacting selectively to a high-pitched voice triggers a high-pitched voice of the user (H1), and (ii) talking to a robot in a high-pitched voice improves the users impression of the robot (H2). The results did not support H1, but marginally supported H2.

A robot that learns in stages utilizing scaffolds: toward an active and long-term human-robot interaction

In recent years, robots began to appear in our daily lives. However, people get bored with them after a short time. We therefore consider that robots that contact with people must have an ability to learn new actions, so that people enjoy the interaction for a long time. However, it is difficult for them to learn complex actions like games through human-robot interaction. If humans learn through human-human interaction, it is known that scaffolding is effective. Scaffolding is a method of promoting learning by gradually giving difficult learning tasks according to the ability of learners. If robots learn through human-robot interaction, it is possible that scaffolding also supports their learning. However, it has not clarified that scaffolding occurs actually through interactions with ordinary people in everyday situations. In this experiment, we clarify this problem, and propose the method of utilizing the scaffold given by ordinary people in everyday situations for robots that contact with people.In recent years, robots began to appear in our daily lives. However, people get bored with them after a short time. We therefore consider that robots that contact with people must have an ability to learn new actions, so that people enjoy the interaction for a long time. However, it is difficult for them to learn complex actions like games through human-robot interaction. If humans learn through human-human interaction, it is known that scaffolding is effective. Scaffolding is a method of promoting learning by gradually giving difficult learning tasks according to the ability of learners. If robots learn through human-robot interaction, it is possible that scaffolding also supports their learning. However, it has not clarified that scaffolding occurs actually through interactions with ordinary people in everyday situations. In this experiment, we clarify this problem, and propose the method of utilizing the scaffold given by ordinary people in everyday situations for robots that contact with people.