Criminal Investigation EIDSS Based on Cooperative Mapping Mechanism

On purpose of improving the research in extension intelligence systems when the knowledge in hand is not sufficient, an intuition evidence model (IEM) based on human-computer cooperative is presented. From the initial intuition process space defined by the primitive experience, a series of interactive mapping learning systems (IMLS) with various reductive levels are created. For, each IELS, the rule sets with respective belief degree are induced and saved. The paper introduces cooperative mapping of intuition evidence and object hypothesesmethod to the criminal investigation, and poses a skeleton of cooperative reasoning. 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 making, 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.

@article{He2014,
  title     = {Criminal Investigation EIDSS Based on Cooperative Mapping Mechanism},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {5},
  number    = {10},
  year      = {2014},
  publisher = {The Science and Information Organization},
  author    = {Ping He},
  doi       = {10.14569/IJACSA.2014.051018},
  url       = {https://doi.org/10.14569/IJACSA.2014.051018}
}