An integrated framework for suspect investigation

In a complex crime scene with many possible suspects and conflicting evidence, crime investigation requires scientific and logical steps to narrow down the suspects. Since human investigators have difficulty in fully handling all reasoning in this highly complex hypothesis space, we propose a decision support system to aid the investigation process. The system integrates a rule-based reasoner, a Bayesian network for criminal profiling, and an assumption-based truth maintenance system for evidential reasoning to determine the most plausible suspect. The reasoning process reasons about a profiling classification and an alibi credibility measure. It proved effective in a realistic simulation.