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dc.contributor.author Keppens, Jeroen
dc.contributor.author Shen, Qiang
dc.contributor.author Price, Christopher John
dc.date.accessioned 2011-09-06T11:02:59Z
dc.date.available 2011-09-06T11:02:59Z
dc.date.issued 2011-08-01
dc.identifier.citation Keppens , J , Shen , Q & Price , C J 2011 , ' Compositional Bayesian Modelling for Computation of Evidence Collection Strategies ' Applied Intelligence , vol 35 , no. 1 , pp. 134-161 . , 10.1007/s10489-009-0208-5 en
dc.identifier.issn 0924-669X
dc.identifier.other PURE: 169540
dc.identifier.other dspace: 2160/7556
dc.identifier.uri http://hdl.handle.net/2160/7556
dc.description J. Keppens, Q. Shen and C. Price. Compositional Bayesian modelling for computation of evidence collection strategies. Applied Intelligence, 35(1):134-161, 2011. en
dc.description.abstract As forensic science and forensic statistics become increasingly sophisticated, and judges and juries demand more timely delivery of more convincing scientific evidence, crime investigation is becoming progressively more challenging. In particular, this development requires more effective and efficient evidence collection strategies, which are likely to produce the most conclusive information with limited available resources. Evidence collection is a difficult task, however, because it necessitates consideration of: a wide range of plausible crime scenarios, the evidence that may be produced under these hypothetical scenarios, and the investigative techniques that can recover and interpret the plausible pieces of evidence. A knowledge based system (KBS) can help crime investigators by retrieving and reasoning with such knowledge, provided that the KBS is sufficiently versatile to infer and analyse a wide range of plausible scenarios. This paper presents such a KBS. It employs a novel compositional modelling technique that is integrated into a Bayesian model based diagnostic system. These theoretical developments are illustrated by a realistic example of serious crime investigation. en
dc.format.extent 28 en
dc.language.iso eng
dc.relation.ispartof Applied Intelligence en
dc.subject Decision support en
dc.subject Compositional modelling en
dc.subject Entropy reduction en
dc.subject Evidence collection en
dc.title Compositional Bayesian Modelling for Computation of Evidence Collection Strategies en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1007/s10489-009-0208-5
dc.contributor.institution Advanced Reasoning Group en
dc.contributor.institution Department of Computer Science en
dc.description.status Peer reviewed en


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