9 April 2007 Collective agents interpolative integral (CAII) for asymmetric threat detection
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Abstract
This paper presents a reasoning system that pools the judgments from a set of inference agents with information from heterogeneous sources to generate a consensus opinion that reduces uncertainty and improves knowledge quality. The system, called Collective Agents Interpolation Integral (CAII), addresses a high level data fusion problem by combining, in a mathematically sound manner, multi-models of inference in knowledge intensive multi agent architecture. Two major issues are addressed in CAII. One is the ability of the inference mechanisms to deal with hybrid data inputs from multiple information sources and map the diverse data sets to a uniform representation in an objective space of reasoning and integration. The other is the ability of the system architecture to allow the continuous and discrete outputs of a diverse set of inference agents to interact, cooperate, and integrate.
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Qiuming Zhu, Stephen O'Hara, Michael Simon, Eric Lindahl, Plamen V. Petrov, "Collective agents interpolative integral (CAII) for asymmetric threat detection", Proc. SPIE 6571, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007, 65710I (9 April 2007); doi: 10.1117/12.718322; https://doi.org/10.1117/12.718322
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