An Intelligent Decision Support System Based on Human-Machine Interaction and Dynamic Track of Psychological Evaluation Criterion

An Intelligent Decision Support System based on man-machine Interaction and Dynamic Track of Psychological Evaluation Criterion is presented in this article. It is shown that a complex decision for a global situation can be disassembled into a series of simple local problems, from which the most satisfactory decision for the local can be found out by individual ways, respectively. At the lower level of total score, the best decision for the local, according to the mathematical interpretation of weight, can be considered as the decision whose distribution of scores is just consistent with the distribution of the psychological weight (or preference) of a decision-maker. At a series of moderate levels, the evaluation criterion is given by human-machine interaction, in which some satisfactory samples are chosen by a decision-maker from a lot of samples, and the barycenter of criterion and the radius of criterion can be estimated by a learning algorithm. In this way the most satisfactory decision for the local made by the decision-maker at each level can be tracked. If we let the collection of satisfactory decisions for the global be the union of the local's most satisfactory decision at all levels, the changing process of psychological criteria, which varies with the change of total score, can be deduced. Finally, a satisfied degree function with which the global consistency of the collection of the local's satisfactory decisions at all levels is given, and a global ranking approach based on the function is presented as well.