Criminal investigation DSS based on extended intuition analysis

On purpose of improving the research in extension intelligence systems when the knowledge in hand is not sufficient, an extension intuition model (EIM) based on human-computer cooperative is presented. From the initial intuition process space defined by the primitive experience, a series of interactive experience learning systems (IELS) with various reductive levels are created. For, each IELS, the rule sets with respective belief degree are induced and saved. The paper introduces experience mapping intuitive inversion (EMII) method to the criminal investigation, and poses a skeleton of cooperative reasoning. Through the trusted intuition relationship construction of practical crime model and on-the-spot model, it sets up a couple of mapping-inversion models in information acquisition of human-computer interaction. The paper views that the reliability of the cooperative 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-formal judgments are essentially needed. When applying the model to reasoning and decision analysis, one can match the intuition judge of the given object to the rule sets of relative nodes, and then draw the conclusion by using some kind of evaluation algorithm. A simple example on how to create and apply the model is give.