Cognitive Architectures and Multi-agent Social Simulation

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4078)


As we know, a cognitive architecture is a domain-generic computational cognitive model that may be used for a broad analysis of cognition and behavior. Cognitive architectures embody theories of cognition in computer algorithms and programs. Social simulation with multi-agent systems can benefit from incorporating cognitive architectures, as they provide a realistic basis for modeling individual agents (as argued in Sun 2001). In this survey, an example cognitive architecture will be given, and its application to social simulation will be sketched.


Explicit Knowledge Agent Model Cognitive Agent Bottom Level Implicit Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anderson, J., Lebiere, C.: The Atomic Components of Thought. Lawrence Erlbaum Associates, Mahwah (1998)Google Scholar
  2. Carley, K., Prietula, M., Lin, Z.: Design versus cognition: The interaction of agent cognition and organizational design on organizational performance. Journal of Artificial Societies and Social Simulation 1(3) (1998)Google Scholar
  3. Cleeremans, A., Destrebecqz, A., Boyer, M.: Implicit learning: News from the front. Trends in Cognitive Sciences 2(10), 406–416 (1998)CrossRefGoogle Scholar
  4. Fodor, J.: The Modularity of Mind. MIT Press, Cambridge (1983)Google Scholar
  5. Gilbert, N.: A simulation of the structure of academic science. Sociological Research Online, 2(2) (1997),
  6. Hirschfield, L., Gelman, S. (eds.): Mapping the Mind: Domain Specificity in Cognition and Culture. Cambridge University Press, Cambridge (1994)Google Scholar
  7. Marr, D.: Vision. W.H. Freeman, New York (1982)Google Scholar
  8. Maslow, A.: Motivation and Personality, 3rd edn. Harper and Row, New York (1987)Google Scholar
  9. Naveh, I., Sun, R.: A cognitively based simulation of academic science. Computational and Mathematical Organization Theory 12(4), 313–337 (2006)CrossRefzbMATHGoogle Scholar
  10. Nelson, T. (ed.): Metacognition: Core Readings. Allyn and Bacon (1993)Google Scholar
  11. Newell, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1990)Google Scholar
  12. Simon, H.: Models of Man, Social and Rational. Wiley, Chichester (1957)zbMATHGoogle Scholar
  13. Sun, R.: Integrating Rules and Connectionism for Robust Commonsense Reasoning. John Wiley and Sons, New York (1994)zbMATHGoogle Scholar
  14. Sun, R.: Cognitive science meets multi-agent systems: A prolegomenon. Philosophical Psychology 14(1), 5–28 (2001)CrossRefGoogle Scholar
  15. Sun, R.: Duality of the Mind. Lawrence Erlbaum Associates, Mahwah (2002)Google Scholar
  16. Sun, R.: A Tutorial on CLARION. Technical report, Cognitive Science Department, Rensselaer Polytechnic Institute (2003),
  17. Sun, R.: Desiderata for cognitive architectures. Philosophical Psychology 17(3), 341–373 (2004)MathSciNetCrossRefGoogle Scholar
  18. Sun, R.: Prolegomena to integrating cognitive modeling and social simulation. In: Sun, R. (ed.) Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation, Cambridge University Press, New York (2006)Google Scholar
  19. Sun, R., Coward, L.A., Zenzen, M.J.: On levels of cognitive modeling. Philosophical Psychology 18(5), 613–637 (2005)CrossRefGoogle Scholar
  20. Sun, R., Naveh, I.: Simulating organizational decision-making using a cognitively realistic agent model. Journal of Artificial Societies and Social Simulation 7(3) (2004),
  21. Sun, R., Peterson, T.: Autonomous learning of sequential tasks: experiments and analyses. IEEE Transactions on Neural Networks 9(6), 1217–1234 (1998)CrossRefGoogle Scholar
  22. Sun, R., Peterson, T.: Multi-agent reinforcement learning: Weighting and partitioning. Neural Networks 12(4-5), 127–153 (1999)CrossRefGoogle Scholar
  23. Watkins, C.: Learning with Delayed Rewards. Ph.D Thesis, Cambridge University, Cambridge, UK (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ron Sun
    • 1
  1. 1.Rensselaer Polytechnic InstituteTroy, NYUSA

Personalised recommendations