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Modeling large military targets is a challenge as they can be complex systems encompassing myriad combinations of
human, technological, and social elements that interact, leading to complex behaviors. Moreover, such targets have
multiple components and structures, extending across multiple spatial and temporal scales, and are in a state of change,
either in response to events in the environment or changes within the system. Complex adaptive system (CAS) theory can
help in capturing the dynamism, interactions, and more importantly various emergent behaviors, displayed by the targets.
However, a key stumbling block is incorporating information from various intelligence, surveillance and reconnaissance
(ISR) sources, while dealing with the inherent uncertainty, incompleteness and time criticality of real world information.
To overcome these challenges, we present a probabilistic reasoning network based framework called complex adaptive
Bayesian Knowledge Base (caBKB). caBKB is a rigorous, overarching and axiomatic framework that models two key
processes, namely information aggregation and information composition. While information aggregation deals with the
union, merger and concatenation of information and takes into account issues such as source reliability and information
inconsistencies, information composition focuses on combining information components where such components may
have well defined operations. Since caBKBs can explicitly model the relationships between information pieces at various
scales, it provides unique capabilities such as the ability to de-aggregate and de-compose information for detailed analysis.
Using a scenario from the Network Centric Operations (NCO) domain, we will describe how our framework can be used
for modeling targets with a focus on methodologies for quantifying NCO performance metrics.
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