An Integrated Framework for Suspect Investigation

In a complex crime scene with many possible suspects and conflicting evidence, crime investigation requires scientific and logical steps to narrow down the suspects. Since human investigators have ...

[1]  Johan de Kleer,et al.  Extending the ATMS , 1986, Artif. Intell..

[2]  G. Palermo,et al.  Constructing Bayesian networks for criminal profiling from limited data , 2008, Knowl. Based Syst..

[3]  S. Ferrari,et al.  Network models of criminal behavior , 2008, IEEE Control Systems.

[4]  Marcus K. Rogers.  The role of criminal profiling in the computer forensics process , 2003, Comput. Secur..

[5]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[6]  Johan de Kleer,et al.  Problem Solving with the ATMS , 1986, Artif. Intell..

[7]  Kathleene M. Heide.  Juvenile involvement in multiple offender and multiple victim parricides , 1993 .

[8]  Johan de Kleer,et al.  An Assumption-Based TMS , 1987, Artif. Intell..

[9]  Hyeonsang Eom,et al.  A compound framework for sports results prediction: A football case study , 2008, Knowl. Based Syst..

[10]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[11]  Jon Doyle,et al.  A Truth Maintenance System , 1979, Artif. Intell..

[12]  Elizabeth A. Olson,et al.  What Makes a Good Alibi? A Proposed Taxonomy , 2004, Law and human behavior.

[13]  Todd K. Shackelford,et al.  Risk of multiple-offender rape–murder varies with female age , 2002 .

[14]  Jeroen Keppens,et al.  A scenario-driven decision support system for serious crime investigation , 2007 .

[15]  Robert I. McKay,et al.  Hybrid Integration of Reasoning Techniques in Suspect Investigation , 2010, IEA/AIE.