Probabilistic abductive computation of evidence collection strategies in crime investigation
Contribuinte(s) |
Department of Computer Science Advanced Reasoning Group |
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Data(s) |
23/01/2008
23/01/2008
2005
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Resumo |
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. 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. Non peer reviewed |
Formato |
11 |
Identificador |
Keppens , J , Schafer , B & Shen , Q 2005 , ' Probabilistic abductive computation of evidence collection strategies in crime investigation ' pp. 215-225 . PURE: 74569 PURE UUID: 543ae7aa-b7a3-4684-b404-1ff52a1f06f4 dspace: 2160/463 |
Idioma(s) |
eng |
Tipo |
/dk/atira/pure/researchoutput/researchoutputtypes/contributiontoconference/paper Conference paper |
Relação | |
Direitos |