Multisource Fusion for Probabilistic Detection and Opportunistic Assessment of Terrorist Threats

Citation: Laskey, K.B. and T.S. Levitt, "Multisource Fusion for Probabilistic Detection and Opportunistic Assessment of Terrorist Threats", presented at Aerosense 2002.


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 multicity 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.

KEY WORDS: Bayesian Networks, Bayesian Network Fragments, Multi-Entity Bayesian Networks, hypothesis space, counter-terrorism, homeland defense, biowarfare, multisource fusion, information fusion.

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