A knowledge-based reasoning model for crime reconstruction and investigation

Abstract

Artificial intelligence has been successfully applied in many areas including forensic sciences. Perhaps all forensic works can be regarded as helping reconstruct crimes, i.e. clarify and sequence the events that took place in the commission of a crime through evidence. However, there are few researches on the crime reconstruction using artificial intelligence methods. In this paper, we present a model based on Bayesian networks to help solve crimes. The model, which is termed ‘case-type based model’, is based on the knowledge of a type of crimes. We use Bayesian networks to represent the knowledge and conduct the uncertainty reasoning. We propose a growth algorithm of Bayesian networks to adapt the model to different cases. The model was tested through a real case, and the results indicate that the model can provide effective investigation suggestions and achieve the crime reconstruction.