Intuitive Learning and Artificial Intuition Networks

Intuition-based learning (IBL) has been used in various problem-solving areas such as risk analysis, medical diagnosis and criminal investigation. However, conventional IBL has the limitation that it has no criterion for choosing the trusted intuition based on the knowledge and experience. The purpose of this paper is to develop a learning model for human-computer cooperative from user's perspective. We have established the theoretical foundation and conceptualization of the constructs for learning system with trusted intuition. And suggest a new machine learning technique called trusted intuition network (TIN). We have developed a general instrument capable of reliably and accurately measuring trusted intuition in the context of intuitive learning systems. We also compare the results with the learning methods, artificial intuition networks and conventional IBL. The results of this paper show that the proposed technique outperforms those of many other methods, it overcomes the limitation of conventional IBL, and it provides improved uncertainty learning theory.