Advertisement

Self-organized and Evolvable Cognitive Architecture for Intelligent Agents and Multi-Agent Systems

  • Oscar Javier Romero López
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

Integrating different kinds of micro-theories of cognition in intelligent systems when a huge amount of variables are changing continuously, with increasing complexity, is a very exhaustive and complicated task. Our approach proposes a hybrid cognitive architecture that relies on the integration of emergent and cognitivist approaches using evolutionary strategies, in order to combine implicit and explicit knowledge representations necessary to develop cognitive skills. The proposed architecture includes a cognitive level controlled by autopoietic machines and artificial immune systems based on genetic algorithms, giving it a significant degree of plasticity. Furthermore, we propose an attention module which includes an evolutionary programming mechanism in charge of orchestrating the hierarchical relations among specialized behaviors, taking into consideration the global workspace theory for consciousness. Additionally, a co-evolutionary mechanism is proposed to propagate knowledge among cognitive agents on the basis of memetic engineering. As a result, several properties of self-organization and adaptability emerged when the proposed architecture was tested in an animat environment, using a multi-agent platform.

Keywords

Cognitive architectures gene expression programming artificial immune systems neural nets memetics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderson, M.L.: Embodied cognition: A field guide. Artificial Intelligence 149(1), 91–130 (2003)CrossRefGoogle Scholar
  2. 2.
    Berthoz, A.: The Brain’s Sense of Movement. Harvard Univ. Press, Cambridge (2000)Google Scholar
  3. 3.
    Vernon, D., Metta, G., Sandini, G.: A survey of artificial cognitive systems: Implications for the autonomous development of mental capabilities in computational agents. IEEE Trans. Evolutionary Computation 11(2) (April 2007)Google Scholar
  4. 4.
    Rosenbloom, P., Laird, J., Newell, A. (eds.): The Soar Papers: Research on integrated intell. MIT Press, Cambridge (1993)Google Scholar
  5. 5.
    Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psy. Rev. 111(4), 1036–1060 (2004)CrossRefGoogle Scholar
  6. 6.
    Shanahan, M.P., Baars, B.: Applying global workspace theory to the frame problem. Cognition 98(2), 157–176 (2005)CrossRefGoogle Scholar
  7. 7.
    Breazeal: Emotion and sociable humanoid robots. Int. J. Human-Computer Studies 59, 119–155 (2003)CrossRefGoogle Scholar
  8. 8.
    Sun, R., Merrill, E., Peterson, T.: From implicit skills to explicit knowledge: A bottom-up model of skill learning. Cognitive Science 25, 203–244 (2001)CrossRefGoogle Scholar
  9. 9.
    Cassimatis, N.L.: Adaptive Algorithmic Hybrids for Human-Level Artificial Intelligence. Advances in Arti. Intell. (2007)Google Scholar
  10. 10.
    Franklin, S.: The LIDA architecture: Adding new modes of learning to an intelligent, autonomous, software agent. In: Proc. of the Int. Conf. on Integrated Design and Process Technology (2006)Google Scholar
  11. 11.
    Maturana, H.R., Varela, F.J.: Autopoiesis and Cognition: The Realization of the Living. Boston Studies on the Philosophy of Science. D. Reidel Publishing Company, Dordrecht (1980)Google Scholar
  12. 12.
    Ferreira, C.: Gene Expression Programming: A new adaptive algorithm for solving problems. Complex Systems 13, 87–129 (2001)zbMATHMathSciNetGoogle Scholar
  13. 13.
    Rumelhart, D., McClelland, J.: Parallel Distributed Processing: Explorations in the Microstructures of Cognition. MIT Press, Cambridge (1986)Google Scholar
  14. 14.
    Watkins, C., Dayan, P.: Q-learning. Machine Learning 3, 279–292 (1992)Google Scholar
  15. 15.
    de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach (2002)Google Scholar
  16. 16.
    Dawkins, R.: The Selfish Gene. Oxford University Press, Oxford (1989)Google Scholar
  17. 17.
    Romero, D., Niño, L.: An Immune-based Multilayered Cognitive Model for Autonomous Navigation, CEC, Vancouver, pp. 1115–1122 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Oscar Javier Romero López
    • 1
  1. 1.Fundación Universitaria Konrad LorenzBogotáColombia

Personalised recommendations