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From Sensorimotor Graphs to Rules: An Agent Learns from a Stream of Experience

  • Marius Raab
  • Mark Wernsdorfer
  • Emanuel Kitzelmann
  • Ute Schmid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6830)

Abstract

In this paper we argue that a philosophically and psychologically grounded autonomous agent is able to learn recursive rules from basic sensorimotor input. A sensorimotor graph of the agent’s environment is generated that stores and optimises beneficial motor activations in evaluated sensor space by employing temporal Hebbian learning. This results in a categorized stream of experience that feeds in a Minerva memory model which is enriched by a time line approach and integrated in the cognitive architecture Psi—including motivation and emotion. These memory traces feed seamlessly into the inductive rule acquisition device Igor2 and the resulting recursive rules are made accessible in the same memory store. A combination of cognitive theories from the 1980ies and state-of-the-art computer science thus is a plausible approach to the still prevailing symbol grounding problem.

Keywords

symbol grounding temporal Hebbian learning cognitive architecture inductive rule learning 

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References

  1. 1.
    Anderson, J.R.: How can the human mind occur in the physical universe? Oxford University Press, Oxford (2007)CrossRefGoogle Scholar
  2. 2.
    Avila Garcez, A.S., Zaverucha, G.: The Connectionist Inductive Learning and Logic Programming System. Applied Intelligence 11, 59–77 (1999)CrossRefGoogle Scholar
  3. 3.
    Bach, J.: Principles of Synthetic Intelligence: PSI: An Architecture of Motivated Cognition. Oxford University Press, Oxford (2009)CrossRefGoogle Scholar
  4. 4.
    Butz, M.: Self-organizing sensorimotor graphs plus internal motivations yield animal-like behavior. Adaptive Behavior 18, 315 (2010)CrossRefGoogle Scholar
  5. 5.
    Dougherty, M.R.P., Gettys, C.F., Ogden, E.E.: MINERVA-DM: A Memory Processes Model for Judgments of Likelihood. Psych. Review 106(1), 180–209 (1999)CrossRefGoogle Scholar
  6. 6.
    Doerner, D.: Bauplan für eine Seele. Rowohlt, Reinbek (1999)Google Scholar
  7. 7.
    Harnad, S.: The Symbol Grounding Problem. Physica D 42, 335–346 (1990)CrossRefGoogle Scholar
  8. 8.
    Heidegger, M.: Sein und Zeit. Niemeyer, Tübingen, 16th edn. (1986)Google Scholar
  9. 9.
    Hintzman, D.L.: MINERVA 2: A simulation model of human memory. Behavior Research Methods, Instruments & Computers 16(2), 96–101 (1984)Google Scholar
  10. 10.
    Kitzelmann, E.: A Combined Analytical and Search-Based Approach to the Inductive Synthesis of Functional Programs. PhD thesis, University of Bamberg (2010)Google Scholar
  11. 11.
    Klahr, D., Wallace, J.: An Information Processing Analysis of Some Piagetian Experimental Tasks. Cognitive Psychology 1, 358–387 (1970)CrossRefGoogle Scholar
  12. 12.
    Laird, J.E.: Extending the Soar cognitive architecture. In: Proceedings of the Artificial General Intelligence Conference, Memphis, TN (2008)Google Scholar
  13. 13.
    Langley, P., Laird, J.E., Rogers, S.: Cognitive architectures: Research issues and challenges. Cognitive Sytems Research 10(2), 141–160 (2009)CrossRefGoogle Scholar
  14. 14.
    Matyas, J.: Random Optimization. Automaton and Remote Control 26(2), 246–253 (1965)MathSciNetGoogle Scholar
  15. 15.
    Mayo, M.: Symbol grounding and its implications for artificial intelligence. In: Proceedings of the 26th Australasian Computer Science Conference, vol. 16, pp. 55–60. Australian Computer Society, Inc. (2003)Google Scholar
  16. 16.
    Mitchell, T.M.: Machine learning. McGraw-Hill, Boston (1997)zbMATHGoogle Scholar
  17. 17.
    Nadel, L.: Distributed Representations. In: C. R. Encyclopedia of Cognitive Science. Nature publishing group, Macmillan (2003)Google Scholar
  18. 18.
    Newberg, A., d’Aquili, E., Rause, V.: Why God Won’t Go Away: Brain Science and the Biology of Belief. Ballantine Books, NY (2002)Google Scholar
  19. 19.
    Porr, B., Wörgötter, F.: Temporal Hebbian learning in Rate-Coded Neural Networks: A theoretical approach towards classical conditioning. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 1115–1120. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  20. 20.
    Rodger, S.H.: JFLAP. An interactive formal languages and automata package. Jones and Bartlett’s, Sudbury (2006)Google Scholar
  21. 21.
    Schmid, U., Hofmann, M., Kitzelmann, E.: Analytical inductive programming as a cognitive rule acquisition devise. In: Proceedings of the Second Conference on Artificial General Intelligence, pp. 162–167. Atlantis Press, Amsterdam (2009)Google Scholar
  22. 22.
    Steels, L.: The Symbol Grounding Problem Has Been Solved. So What’s Next? In: de Vega, M. (ed.) Symbols and Embodiment: Debates on Meaning and Cognition, ch. 12. Oxford University Press, Oxford (2008)Google Scholar
  23. 23.
    Sun, R.: The CLARION cognitive architecture: Extending cognitive modeling to social simulation. In: Sun, R. (ed.) Cognition and Multi-Agent Interaction. Cambridge University Press, New York (2006)Google Scholar
  24. 24.
    Sun, R.: The motivational and metacognitive control in CLARION. In: Gray, W.D. (ed.) Integ. Models of Cog. Systems. Oxford University Press, New York (2007)Google Scholar
  25. 25.
    Vanpaemel, W., Storms, G.: In search of abstraction: The varying abstraction model of categorization. Psychonomic Bulletin & Review 15(4), 732 (2008)CrossRefGoogle Scholar
  26. 26.
    Wallace, J., Klahr, D., Bluff, K.: A Self-Modifying Production System Model of Cognitive Development. In: Klahr, D., Langley, P., Neches, R.T. (eds.) Prod. System Models of Learning and Development, pp. 359–435. MIT Press, Cambridhge (1987)Google Scholar
  27. 27.
    Watson, D.: Computing the n-dimensional Delaunay tessellation with application to Voronoi polytopes. The Computer Journal 24(2), 167 (1981)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Winograd, T., Flores, F.: Understanding Computers and Cognition. A New Foundation for Design. Ablex Corporation, Norwood (1986)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marius Raab
    • 1
  • Mark Wernsdorfer
    • 1
  • Emanuel Kitzelmann
    • 2
  • Ute Schmid
    • 1
  1. 1.Cognitive Systems GroupUniversity of BambergGermany
  2. 2.International Computer Science InstituteBerkeleyUSA

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