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Proceedings Paper

Multisource fusion for opportunistic detection and probabilistic assessment of homeland terrorist threats
Author(s): Kathryn Blackmond Laskey; Tod S. Levitt
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Paper Abstract

Bayesian Network Fragments (BNFrags) provide a practical, computational methodology to encode a distributed library of computer-usable knowledge patterns for automated reasoning about aspects of homeland defense against terrorism. Multi-Entity Bayesian Networks provide a means of encoding repeated patterns and relationships in the form of BNFrags having variables that range over entities of a given type. New evidence either is matched to existing entities or triggers new entities to be hypothesized. BNFrag instances are created by replacing the variables by the names of entities in the situation. These BNFrags are combined to form situation-specific Bayesian networks (SSNs). We propose the use of MEBNs as the inferential cornerstone of a cumulative national, distributed knowledge base (KB) for homeland defense. In this paper we illustrate the use of MEBNs for these purposes with an example concerning a multi-city coordinated biowarfare attack. We show how current trends in the use of on-line reporting by health care and related facilities has the potential to enable opportunistic detection of and response to low probability, high consequence events for which it would otherwise be a practical impossibility to set up specifically directed monitoring capabilities.

Paper Details

Date Published: 14 August 2002
PDF: 10 pages
Proc. SPIE 4708, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Defense and Law Enforcement, (14 August 2002); doi: 10.1117/12.479295
Show Author Affiliations
Kathryn Blackmond Laskey, George Mason Univ. (United States)
Tod S. Levitt, Information Extraction and Transport, Inc. (United States)


Published in SPIE Proceedings Vol. 4708:
Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Defense and Law Enforcement
Edward M. Carapezza, Editor(s)

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