No Representation Without Integration! Better Cognitive Modeling Through Interoperability

  • Walter WarwickEmail author
  • Christian Lebiere
  • Stuart Rodgers
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 780)


Historically, cognitive modeling has been an exercise in theory confirmation. “Cognitive architectures” were advanced as computational instantiations of theories that could be used to model various aspects of cognition and then be put to empirical test by comparing the simulation-based predictions of the model against the actual performance of human subjects. More recently, cognitive architectures have been recognized as potentially valuable tools in the development of software agents—intelligent routines that can either mimic or support human performance in complex domains. While the introduction of cognitive architectures to what has been regarded as the exclusive province of artificial intelligence is a welcome turn, the history of cognitive modeling casts a long shadow. In particular, there is a tendency to apply cognitive architectures as monolithic, one-off solutions. This runs counter to many of the best practices of modern software engineering, which puts a premium on developing modular and reusable solutions. This paper describes the development of a novel software infrastructure that supports interoperability among cognitive architectures.


Human behavior representation Cognitive architectures Model integration 


  1. 1.
    Bishop, M., et al.: Insider threat identification by process analysis. In: IEEE Security and Privacy Workshops, pp. 251–264. IEEE, San Josep (2014)Google Scholar
  2. 2.
    Anderson, J.R., Lebiere, C.: The Atomic Components of Thought. Erlbaum, Mahwah (1998)Google Scholar
  3. 3.
    Newel, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1994)Google Scholar
  4. 4.
    Gluck, K.A.: Cognitive architectures for human factors in aviation. In: Salas, E., Maurino, D. (eds.) Human Factors in Aviation, 2nd edn, pp. 375–400. Elsevier, New York (2010)CrossRefGoogle Scholar
  5. 5.
    John, B.E., et al.: Predictive human performance modeling made easy. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 455–462. Association for Computing Machinery (2004)Google Scholar
  6. 6.
    Pew, R., Mavor, A.S. (eds.): Modeling Human and Organizational Behavior: Applications to Military Simulations. National Academy Press, Washingto, D.C. (1998)Google Scholar
  7. 7.
    Stacy, W., et al.: Agents from the future. In: I/ITSEC. National Training and Simulation Association, Orlando, FL (2017)Google Scholar
  8. 8.
    Pew, R., Gluck, K.A. (eds.): Modeling Human Behavior with Integrated Cognitive Architectures: Comparison, Evaluation, and Validation. Lawrence Erlbaum Associates, Mahwah (2005)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Walter Warwick
    • 1
    Email author
  • Christian Lebiere
    • 2
  • Stuart Rodgers
    • 1
  1. 1.TiER1 Performance Solutions, LLCCovingtonUSA
  2. 2.Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations