Studies of human-computer interaction system based on trust intuition learning theory
Weidong Tao · 2010
On purpose of improving the research in human-computer interaction system (HCIS) when the information in hand is not sufficient, a learning model of relationship mapping intuition discovered (RMID) based on experience process is presented. The paper introduces a method that experience relationship mapping and intuition inversion to the decision making, and poses a skeleton of intuitive reasoning. Through the relationship construction of practical intuitive model and on-the-spot model, it sets up a couple of mapping models intuitive information acquisition. The study shows that the premise of automatic reasoning is to set up patterns of intuitive sub-optimum relationship. The paper views that the reliability of the automatic reasoning depends on the human-computer interaction results. Simultaneously, choosing the case-cracking clue should be determined by comprehensive evaluations and self-learning of intuition or sub-optimum judgments are essentially needed. A simple example on how to create and apply the model is give. The presented model can be applied conveniently by selecting suitable HCIS in accordance with the give intuitive judge and computing the best decision from the rules in those HCIS.