A Design of Criminal Investigation Expert System Based on CILS
Ping He, Weidong Tao · Journal of Software · 2011
Abstract—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 a cooperative intuition learning system (CILS) which employs domain knowledge of recidivism in the crime analysis system. Using the elicited domain knowledge, the CILS 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 (EMII), and we describe the rationale for developing the CIESs, why we focus on the criminal analysis system, the methodology for eliciting CILS 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 CILS 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 Index Terms—CIESs; extension intelligence; intuition learning system; RMI; cooperative intuition reasoning I.