Abstract
In mechatronics and robotics, one of the important issues is to design human interface. There are two issues on interaction design research. One is the way to education and training to adapt humans for operating the robots or interaction systems. Another one is the way to make interaction design adaptable for humans. This chapter research at the latter issue. This chapter describes the interactive learning system to assist positive change in the preference of a human toward the true preference; then evaluation of the awareness effect is discussed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results showed that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also showed that the system improves the efficiency for deciding the most preferred plan for both heavy users and light users.
TopIntroduction
The Role of Mechatronics and Robotics in This Research
In Mechatronics and Robotics, one of the important issues is to design Human interface. As making progress on Mechatronics and computer technology, robots will become more integrated into our daily lives. Thus, interaction design between humans and systems is one of the main research issues on Human-robot interaction or Human computer interaction. To realize this issue, Habib (2006, 2007) discusses that a unifying interdisciplinary and intelligent engineering paradigm is needed. There are two issues on interaction design research. One is the way to education and training to adapt humans for operating the robots or interaction systems. It is a machine-centered interaction design. For example, after acquiring the skills of automobile driving, the human is able to drive a car. Another one is the way to make interaction design adaptable for humans. In other words, it is a human-centered interaction design. This research aims at latter issue.
Key Terms in this Chapter
Visualizing the User’s Preference Trace: The objective of this visualization is to show the distribution and the degree of the user’s preference to him/herself.
Interactive Recommendation Space: In the recommendation space, the user can view and select various plans actively. The recommendation space consists of two dimensions, the preference reduction axis and the preference extension axis, in that, various plans are arranged in a plane.
Light Users: Users of the interactive recommendation system who do not watch all plans since they stop watching when a preferred plan is found.
Preference Change Problem: It is a problem that the collected user's profile is not same as the user’s current preference.
Human Adaptive and Friendly: Less active but more intelligent agent is desirable since it does not seem to be officious for the human.
Visualizing the Recommendation Space: The objective of this visualization is to inform a user of two kinds of information. First is that the recommendation space consists of two-axes. Second is that in each axis, groups or plans are ordered according to the recommendation order.
Interactive Reinforcement Learning with Human: Reinforcement learning method in that reward function denoting goal is given interactively by a human. It is not easy to keep reward function being fixed while the human gives rewards for the reinforcement learning agent. It is that the reward function may not be fixed for the learning algorithm if an end-user changes his/her mind or his/her preference.
Heavy Users: Users of the interactive recommendation system who decide the most preferred plan after watching almost all plans.
Model of a User’s Preference Shift: It is defined by two axes, preference reduction and preference extension. Comparing the previous preference set and the current preference set, the common set is the invariant preference, the reduction set is called preference reduction, and the addition set is called preference extension.
Awareness Based Recommendation: It is the user-centered recommendation by visualizing both the recommendation space with prepared recommendation plans and the user’s preference trace as the history of the recommendation in it. The recommendation space visualizes the possible preference shift of the user.