Probabilistic abductive computation of evidence collection strategies in crime investigation

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dc.contributor.author Keppens, Jeroen
dc.contributor.author Schafer, Burkhard
dc.contributor.author Shen, Qiang
dc.date.accessioned 2008-01-23T11:41:35Z
dc.date.available 2008-01-23T11:41:35Z
dc.date.issued 2005
dc.identifier.citation Keppens , J , Schafer , B & Shen , Q 2005 , ' Probabilistic abductive computation of evidence collection strategies in crime investigation ' pp. 215-225 . en
dc.identifier.other PURE: 74569
dc.identifier.other dspace: 2160/463
dc.identifier.uri http://hdl.handle.net/2160/463
dc.identifier.uri http://doi.acm.org/10.1145/1165485.1165520 en
dc.description J. Keppens, Q. Shen and B. Schafer. Probabilistic abductive computation of evidence collection strategies in crime investigation. Proceedings of the 10th International Conference on Artificial Intelligence and Law, pages 215-225. en
dc.description.abstract This paper presents a methodology for integrating two approaches to building decision support systems (DSS) for crime investigation: symbolic crime scenario abduction [16] and Bayesian forensic evidence evaluation [5]. This is achieved by means of a novel compositional modelling technique that allows for automatically generating a space of models describing plausible crime scenarios from given evidence and formally represented domain knowledge. The main benefit of this integration is that the resulting DSS is capable to formulate effective evidence collection strategies useful for differentiating competing crime scenarios. A running example is used to demonstrate the theoretical developments. en
dc.format.extent 11 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Probabilistic abductive computation of evidence collection strategies in crime investigation en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en


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