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

Keppens, Jeroen and Zeleznikow, John (2003) A Model Based Reasoning Approach for Generating Plausible Crime Scenarios from Evidence. In: Proceedings of the Ninth International Conference on Artificial Intelligence and Law. ACM Press, New York, pp. 51-59.

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Abstract

Robust decision support systems (DSSs) for crime investigation are dfficult 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.

Item Type: Book Section
ISBN: 1581137478
Additional Information:

ICAIL ‘03 , June 24-28, 2003, Edinburgh, Scotland, UK.

Uncontrolled Keywords: ResPubID5369 expert system, assumption based truth maintenance system, ATMS, model based reasoning DSS, inference engine, query handler, scenario instantiator
Subjects: Faculty/School/Research Centre/Department > School of Management and Information Systems
RFCD Classification > 280000 Information, Computing and Communication Sciences
Depositing User: VUIR
Date Deposited: 04 Sep 2013 05:53
Last Modified: 04 Sep 2013 05:53
URI: http://vuir.vu.edu.au/id/eprint/10642
DOI: https://doi.org/10.1145/1047788.1047796
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Citations in Scopus: 27 - View on Scopus

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