Uncertainty Representation and Reasoning in Complex Systems

  • Kathryn Blackmond Laskey
  • Paulo Cesar G. Costa
Part of the Studies in Computational Intelligence book series (SCI, volume 168)


The rapid expansion of corporate computer networks, the rise of the World Wide Web (WWW), and exploding computational power are some of the most visible innovations shaping our increasingly knowledge-based society. The growing demand for interconnectivity and interoperability gives rise to systems of ever-greater complexity. These include systems of systems, whose subsystems are systems in their own right, often geographically distributed and exhibiting ownership and/or managerial independence. Along with the increasing complexity of systems comes a growing demand for systems that act intelligently and adaptively in response to their environments. There is a need for systems that can process incomplete, uncertain and ambiguous information, and can learn and adapt to environments that require interoperating with other intelligent, adaptive complex systems.


Uncertainty probabilistic ontologies Bayesian inference plausible reasoning incomplete information artificial intelligence 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kathryn Blackmond Laskey
    • 1
  • Paulo Cesar G. Costa
    • 2
  1. 1.Department of Systems Engineering and Operations Research, MS 4A5George Mason UniversityFairfaxUSA
  2. 2.Center of Excellence in C4I MSN 4B5George Mason UniversityFairfaxUSA

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