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Proceedings Paper

A computational framework for modeling targets as complex adaptive systems
Author(s): Eugene Santos; Eunice E. Santos; John Korah; Vairavan Murugappan; Suresh Subramanian
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Paper Abstract

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.

Paper Details

Date Published: 4 May 2017
PDF: 20 pages
Proc. SPIE 10206, Disruptive Technologies in Sensors and Sensor Systems, 102060H (4 May 2017); doi: 10.1117/12.2268821
Show Author Affiliations
Eugene Santos, Thayer School of Engineering, Dartmouth College (United States)
Eunice E. Santos, Illinois Institute of Technology (United States)
John Korah, Illinois Institute of Technology (United States)
Vairavan Murugappan, Illinois Institute of Technology (United States)
Suresh Subramanian, Illinois Institute of Technology (United States)


Published in SPIE Proceedings Vol. 10206:
Disruptive Technologies in Sensors and Sensor Systems
Russell D. Hall; Misty Blowers; Jonathan Williams, Editor(s)

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