Criminal Investigation Expert System Based on Extension Intelligence

摘要

Based on the theoretical research and actual developing status of artificial intelligence and expert system, this paper discusses several issues in the development of criminal investigation expert systems (CIESs). In particular, we focus on an extension decision support system (EDSS) which employs domain knowledge of recidivism in the crime analysis system. Using the elicited domain knowledge, the EDSS tool uses deductive reasoning techniques to make inferences and provide suggestive courses of action to support the investigatory functions of police, attorneys, or probation officials. In this paper, we present an experience mapping intuitive inversion principle (EMU), and we describe the rationale for developing the CIESs, why we focus on the criminal analysis system, the methodology for eliciting EDSS domain knowledge and experience, and a scenario of what we are implementing as a proof of intuition learning system. A series of elicitation sessions which epitomize the EDSS have been discussed in the paper. After presenting an overview of the system and the major research choices, we describe in detail the system's modules and present examples of its potential use.