Abstract
Crime investigation is a challenging and difficult task, especially if there are many suspects, together with inconsistencies between witnesses, alibis and physical evidence. We propose integration of a rule-based reasoner and a Bayesian network profiler through an Assumption-based Truth Maintenance System (ATMS) to determine the most plausible suspect. The process combines a profiling classification and an alibi credibility measure. It showed effective results in a simulation.
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Kim, K., Choi, H., McKay, R.(. (2010). Hybrid Integration of Reasoning Techniques in Suspect Investigation. In: GarcÃa-Pedrajas, N., Herrera, F., Fyfe, C., BenÃtez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13022-9_65
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DOI: https://doi.org/10.1007/978-3-642-13022-9_65
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