Interactive Learning Models based on extension intelligence

The paper describes interactive learning system (ILS) for intuition judgement and data analysis. The selection of Interactive Learning Models is performed under both intuition and data analysis learning. 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 interactive learning models and 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 man-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 ILS in accordance with the give intuitive judge and computing the best decision from the rules in those ILS.