Advertisement

Architecture for Machine Learning Techniques to Enable Augmented Cognition in the Context of Decision Support Systems

  • David Martinez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8534)

Abstract

For a wide range of applications, one of the key challenges is to identify an architecture that is suitable for machine learning techniques to enable important augmented cognition capabilities in the context of complex decision support systems. This overview paper presents an architecture framework. The elements of the architecture are described starting with data formatting, a machine learning algorithm taxonomy, components of courses of action, resource management, and finally the role of augmented cognition within the architecture. The paper includes one cyber security example where the architecture framework is employed. The paper concludes with future work in the development of a recommender system.

Keywords

Machine learning decision support systems human-machine interfaces recommender system 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Braun, J., Glina, Y.: Hybrid Methods for Multisource Information Fusion and Decision Support. In: Proc. SPIE, vol. 6242 (2006)Google Scholar
  2. 2.
    Chryssanthacopoulos, J.P., Kochenderfer, M.J.: Decomposition Methods for Optimized Collision Avoidance with Multiple Threats. Journal of Guidance, Control, and Dynamics 35(2), 398–405 (2012)CrossRefGoogle Scholar
  3. 3.
    Crouser, J.R., Ottley, A., Chang, R.: Balancing Human and Machine Contributions in Human Computation Systems. In: Human Computation Handbook. Springer (2013)Google Scholar
  4. 4.
    Miller, B.A., Arcolano, N., Bliss, N.T.: Efficient Anomaly Detection in Dynamic, Attributed Graphs. In: IEEE International Conference on Intelligence and Security Informatics, Seattle, WA (June 2013)Google Scholar
  5. 5.
    Blum, A.: Random Projection, Margins, Kernels, and Feature-Selection. In: Saunders, C., Grobelnik, M., Gunn, S., Shawe-Taylor, J. (eds.) SLSFS 2005. LNCS, vol. 3940, pp. 52–68. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • David Martinez
    • 1
  1. 1.Lincoln LaboratoryMassachusetts Institute of TechnologyLexingtonU.S.A.

Personalised recommendations