Advertisement

Genuine representation in artificial systems

  • Mark H. Bickhard
Philosophy of Artificial Intelligence
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1502)

Abstract

The greatest challenge to a model of the emergence of representation is that of the normativity of representations: the possibility of being true or false. The strongest version of that challenge is to be able to account for system detectable representational error, as is used in error guided behavior or error guided learning. No model in the standard literature, and, arguably, no spectator model of any kind, can account for it. Genuine representation, however, with content and truth value—system detectable truth value—emerges in the selection of actions and interactions in autonomous agents, whether natural or artificial, organisms or robots. Representation is most fundamentally of future potentialities for interaction, rather than of past encounters as standard approaches would have it. Representation is intrinsic to agents, not to passive spectators. The fundamental aspirations of Artificial Intelligence to create genuine artificial minds will be met in robotics.

Keywords

Representation pragmatism robots agents emergence 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Beer, R. D. (1990). Intelligence as Adaptive Behavior. Academic.Google Scholar
  2. Beer, R. D. (1995a). Computational and Dynamical Languages for Autonomous Agents. In R. Port, T. J. van Gelder (Eds.), Mind as Motion: Dynamics, Behavior, and Cognition. (121–147) Cambridge, MA: MIT Press.Google Scholar
  3. Beer, R. D. (1995b). A Dynamical Systems Perspective on Agent-Environment Interaction. Artificial Intelligence, 73(1/2), 173.CrossRefGoogle Scholar
  4. Bickhard, M. H. (1980). Cognition, Convention, and Communication. New York: Praeger.Google Scholar
  5. Bickhard, M. H. (1982). Automata Theory Artificial Intelligence, and Genetic Epistemology. Revue Internationale de Philosophie, 36(142–143), 549–566.Google Scholar
  6. Bickhard, M. H. (1988). Piaget on Variation and Selection Models: Structuralism, Logical Necessity, and Interactivism. Human Development, 31, 274–312.CrossRefGoogle Scholar
  7. Bickhard, M. H. (1992). How Does the Environment Affect the Person? In L. T. Winegar, J. Valsiner (Eds.) Children’s Development within Social Context: Metatheory and Theory. (63–92). Hillsdale, NJ: Erlbaum.Google Scholar
  8. Bickhard, M. H. (1993). Representational Content in Humans and Machines. Journal of Experimental and Theoretical Artificial Intelligence, 5, 285–333.Google Scholar
  9. Bickhard, M. H. (1996). Troubles with Computationalism. In W. O’Donohue, R. F. Kitchener (Eds.) The Philosophy of Psychology. (173–183). London: Sage.Google Scholar
  10. Bickhard, M. H. (1997). Is Cognition an Autonomous Sybsystem? In S. Ó Nuall’in, in P. Mc Kevitt, E. MacAog’in, Two Sciences of Mind: Readings in Cognitive Science and Consciousness. (115–131). Amsterdam: John Benjamins.Google Scholar
  11. Bickhard, M. H. (in press). Levels of Representationality. Journal of Experimental and Theoretical Artificial Intelligence.Google Scholar
  12. Bickhard, M. H., Campbell, R. L. (1989). Interactivism and Genetic Epistemology. Archives de Psychologie, 57(221), 99–121.Google Scholar
  13. Bickhard, M. H., Campbell, R. L. (1996a). Developmental Aspects of Expertise: Rationality and Generalization. Journal of Experimental and Theoretical Artificial Intelligence, 8(3/4), 399–417.CrossRefGoogle Scholar
  14. Bickhard, M. H., Campbell, R. L. (1996b). Topologies of Learning and Development. New Ideas in Psychology, 14(2), 111–156.CrossRefGoogle Scholar
  15. Bickhard, M. H., Richie, D. M. (1983). On the Nature of Representation: A Case Study of James J. Gibson’s Theory of Perception. New York: Praeger.Google Scholar
  16. Bickhard, M. H., Terveen, L. (1995). Foundational Issues in Artificial Intelligence and Cognitive Science—Impasse and Solution. Amsterdam: Elsevier Scientific.Google Scholar
  17. Brooks, R. A. (1991a). Intelligence without Representation. Artificial Intelligence, 47(1–3), 139–159.CrossRefGoogle Scholar
  18. Brooks, R. A. (1991b). Challenges for Complete Creature Architectures. In J.-A. Meyer, S. W. Wilson (Eds.) From Animals to Animats. (434–443). MIT Press.Google Scholar
  19. Brooks, R. A. (1991c). New Approaches to Robotics. Science, 253(5025), 1227–1232.CrossRefGoogle Scholar
  20. Brooks, R. A. (1994). Session on Building Cognition. Conference on The Role of Dynamics and Representation in Adaptive Behaviour and Cognition. University of the Basque Country, San Sebastian, Spain, December 9, 1994.Google Scholar
  21. Campbell, R. L. (this session).Google Scholar
  22. Carlson, N. R. (1986). Physiology of Behavior. Boston: Allyn and Bacon.Google Scholar
  23. Cherian, S., Troxell, W. O. (1995). Intelligent behavior in machines emerging from a collection of interactive control structures. Computational Intelligence, 11(4), 565–592. Blackwell Publishers. Cambridge, Mass. and Oxford, UK.Google Scholar
  24. Cherian, S., Troxell, W. O. (1995). Interactivism.: A Functional Model of Representation for Behavior-Based Systems. In Moran, F., Moreno, A., Merelo, J. J., Chacon, P. Advances in Artificial Life: Proceedings of the Third European Conference on Artificial Life, Granada, Spain. (691–703). Berlin: Springer.Google Scholar
  25. Christensen, W. D., Collier, J. D., Hooker, C. A. (in preparation). Autonomy, Adaptiveness, Anticipation: Towards autonomy-theoretic foundations for life and intelligence in complex adaptive self-organising systems.Google Scholar
  26. Clark, A. (1997). Being There. MIT/Bradford.Google Scholar
  27. Clark, A., Toribio, J. (1995). Doing without Representing?. Synthese, 101, 401–431.CrossRefGoogle Scholar
  28. Fodor, J. A. (1998). Concepts: Where Cognitive Science Went Wrong., Oxford: Oxford University Press.Google Scholar
  29. Hooker, C. A. (1995). Reason, Regulation and Realism: Toward a Naturalistic, Regulatory Systems Theory of Reason. Albany, N.Y.: State University of New York Press.Google Scholar
  30. Hooker, C. A. (1996). Toward a naturalised cognitive science. In R. Kitchener and W O’Donohue (Eds.) Psychology and Philosophy. (184–206). London: Sage.Google Scholar
  31. Hookway, C. (1992) Scepticism. London: Routledge.Google Scholar
  32. Joas, H. (1993). American Pragmatism and German Thought: A History of Misunderstandings. In H. Joas Pragmatism and Social Theory. (94–121). University of Chicago Press.Google Scholar
  33. Loewer, B., Rey, G. (1991). Meaning in Mind: Fodor and his critics. Oxford: Blackwell.Google Scholar
  34. Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the Structure of Behavior. New York: Holt, Reinhart, and Winston.Google Scholar
  35. Port, R., van Gelder, T. J. (1995). Mind as Motion: Dynamics, Behavior, and Cognition. Cambridge, MA: MIT Press.Google Scholar
  36. Rescher, N. (1980). Scepticism. Totowa, NJ: Rowman and Littlefield.Google Scholar
  37. Rosenthal, S. B. (1983). Meaning as Habit: Some Systematic Implications of Peirce’s Pragmatism. In E. Freeman (Ed.) The Relevance of Charles Peirce. (312–327). La Salle, IL: Monist.Google Scholar
  38. Sanches, F. (1988/1581). That Nothing is Known. Cambridge.Google Scholar
  39. Stein, L. A. (1994). Imagination and Situated Cognition. Journal of Experimental and Theoretical Artificial Intelligence, 6, 393–407.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Mark H. Bickhard
    • 1
  1. 1.Cognitive ScienceLeHigh UniversityBethlehemUSA

Personalised recommendations