Affiliations: [a] Graduate School of Engineering, Chubu University, Aichi, Japan | [b] Aichi Kiwami College of Nursing, Aichi, Japan
Corresponding authors: Takaya Ogiso, Koichiro Yamauchi, Department of Computer Science, Chubu University, 1200, Matsu-moto-cho, Kasugai-shi, Aichi, Japan. E-mail:firstname.lastname@example.org;email@example.com
Abstract: Artificial intelligence systems are frequently used to solve various
problems in our daily lives.
However, these systems require problem-specific
big data to facilitate their learning processes.
Unfortunately, for unknown environments,
there are no previous instances available for learning.
To support such learning in unknown environments,
we propose a novel hybrid learning system that facilitates collaborative
learning between humans and artificial intelligence systems.
In this study, we verified that the proposed system accelerated both human and
machine learning by employing a simplified color design task.
Moreover, we also improved the system to enable it to select the best answer
from the solution candidates by using masters to evaluate these solution candidates.
The system performance was evaluated using both a simulation and a psychological test comprising a color design task.
Keywords: Collaborative learning, accelerated learning, human skill, general regression neural network