Co-learning system for humans and machines using a weighted majority-based method

摘要

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.