A model based reasoning approach for generating plausible crime scenarios from evidence

  • Authors:

  • Jeroen Keppens;John Zeleznikow

  • Affiliations:

  • The University of Edinburgh, Edinburgh, UK;The University of Edinburgh, Edinburgh, UK

  • Venue:
  • ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
  • Year:
  • 2003

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Abstract

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 any explicit reasoning about crime scenarios. They focus on problems such as intelligence analysis 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 match the case under investigation in many different ways. The approach presented herein is applied to and illustrated with examples from an application of the differentiation between homicidal, suicidal, accidental and natural death.