A Model Based Reasoning Approach for Generating Plausible Crime Scenarios from Evidence

Robust decision support systems (DSSs) for crime investigation are difficult to construct because of the almost infinite variation of plausible crime scenarios. Thus, existing approaches avoid reasoning about crime scenarios explicitly and they focus on problems that do not require this instead (e.g. analysis of intelligence and profiling). This paper introduces a novel model based reasoning technique that enables DSSs to automatically construct representations of crime scenarios. It achieves this by storing the component events of the scenarios instead of entire scenarios and by providing an algorithm that can instantiate and compose these component events into useful scenarios. This approach is more adaptable to unanticipated cases than one that represents scenarios explicitly because it allows component events to be composed in many different ways to match the case under investigation. The approach presented herein is applied to and illustrated with examples from an application ...

• Type: Conference
• Year: 2003
• Where: ICAIL
• Authors: Jeroen Keppens, John Zeleznikow
• Keywords: Crime Scenarios, ICAIL 2003, Model Based Reasoning, Plausible Crime Scenarios