Title :
A knowledge-based fatal incident decision model
Author :
Manivannan, S. ; Guthrie, S.
Author_Institution :
Sch. of Ind. & Syst. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
fDate :
8/1/1994 12:00:00 AM
Abstract :
A methodology for determining remains identification (ID) following a mass disaster is presented. The solution methodology is domain-independent and capable of addressing a wide range of assignment problems. A knowledge-based fatal incident decision model (FINDM) for providing a decision support to forensic scientists involved in the skeletal ID process is discussed. A mathematical framework for FINDM is developed that integrates a knowledge base with a network flow algorithm for resolving conflicts during the ID process. The FINDM framework has been implemented can an IBM PC and includes an observation advisor, an assignment advisor, and a conflict resolution module. Knowledge acquisition and representation issues are discussed, along with a numerical example and results. With respect to the remains ID problem, the FINDM approach shifts major efforts in resolving the problem from that of establishing a method of assignment to that of controlling the quality of data collected, improving domain knowledge, and analyzing conflicts
Keywords :
anthropology; decision support systems; disasters; emergency services; expert systems; knowledge acquisition; knowledge representation; pattern recognition; FINDM; IBM PC; antemortem data; assignment advisor; conflict resolution; contradiction factor; decision support; domain knowledge,; forensic anthropology; forensic scientists; knowledge acquisition; knowledge base; knowledge representation; knowledge-based fatal incident decision model; mass disaster; network flow algorithm; observation advisor; postmortem analysis; regression equations; remains identification; skeletal ID process; trait evaluations; Computer crashes; Data analysis; Earthquakes; Equations; Forensics; Humans; Knowledge acquisition; Large-scale systems; Systems engineering and theory; US Government;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on