Collective agents interpolative integral (CAII) for asymmetric threat detection

Qiuming Zhu; Stephen O'Hara; Michael Simon; Eric Lindahl; Plamen V. Petrov

doi:10.1117/12.718322

9 April 2007 Collective agents interpolative integral (CAII) for asymmetric threat detection

Qiuming Zhu, Stephen O'Hara, Michael Simon, Eric Lindahl, Plamen V. Petrov

Proceedings Volume 6571, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007; 65710I (2007) https://doi.org/10.1117/12.718322
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

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.

Citation Download Citation

Qiuming Zhu, Stephen O'Hara, Michael Simon, Eric Lindahl, and 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); https://doi.org/10.1117/12.718322

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