A computational framework for modeling targets as complex adaptive systems

Eugene Santos, Eunice E. Santos, John Korah, Vairavan Murugappan, Suresh Subramanian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationDisruptive Technologies in Sensors and Sensor Systems
EditorsRussell D. Hall, Misty Blowers, Jonathan Williams
PublisherSPIE
ISBN (Electronic)9781510609136
DOIs
StatePublished - Jan 1 2017
Externally publishedYes
EventDisruptive Technologies in Sensors and Sensor Systems 2017 - Anaheim, United States
Duration: Apr 11 2017Apr 12 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10206
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceDisruptive Technologies in Sensors and Sensor Systems 2017
CountryUnited States
CityAnaheim
Period4/11/174/12/17

Keywords

  • Bayesian knowledge bases
  • Complex adaptive systems
  • Information aggregation
  • Information composition
  • Network centric operations
  • Self-synchronization
  • Target modeling

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'A computational framework for modeling targets as complex adaptive systems'. Together they form a unique fingerprint.

  • Cite this

    Santos, E., Santos, E. E., Korah, J., Murugappan, V., & Subramanian, S. (2017). A computational framework for modeling targets as complex adaptive systems. In R. D. Hall, M. Blowers, & J. Williams (Eds.), Disruptive Technologies in Sensors and Sensor Systems [102060H] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10206). SPIE. https://doi.org/10.1117/12.2268821