Minds and Machines

, Volume 17, Issue 4, pp 369–389 | Cite as

A Praxical Solution of the Symbol Grounding Problem

Article

Abstract

This article is the second step in our research into the Symbol Grounding Problem (SGP). In a previous work, we defined the main condition that must be satisfied by any strategy in order to provide a valid solution to the SGP, namely the zero semantic commitment condition (Z condition). We then showed that all the main strategies proposed so far fail to satisfy the Z condition, although they provide several important lessons to be followed by any new proposal. Here, we develop a new solution of the SGP. It is called praxical in order to stress the key role played by the interactions between the agents and their environment. It is based on a new theory of meaning—Action-based Semantics (AbS)—and on a new kind of artificial agents, called two-machine artificial agents (AM²). Thanks to their architecture, AM2s implement AbS, and this allows them to ground their symbols semantically and to develop some fairly advanced semantic abilities, including the development of semantically grounded communication and the elaboration of representations, while still respecting the Z condition.

Keywords

Action-based semantics Artificial evolution Communication Hebb’s rule Local selection Symbol Grounding Problem Two-machine artificial agents Zero semantic commitment condition 

References

  1. Barklund, J., et al. (2000). Reflection principles in computational logic. Journal of Logic and Computation, 10, 743–786.MATHCrossRefMathSciNetGoogle Scholar
  2. Brazier, F. M. T., et al. (1999). Compositional modelling of reflective agents. International Journal of Human-Computer Studies, 50, 407–431.CrossRefGoogle Scholar
  3. Brooks, R. A. (1990). Elephants don’t play chess. Robotics and Autonomous Systems, 6, 3–15.CrossRefGoogle Scholar
  4. Cointe P. (Ed.) (1999). Meta-Level architectures and reflection, second international conference on reflection. Saint-Malo, France: Springer-Verlag.Google Scholar
  5. Donahoe J. W., et al. (Eds.) (1997). Neural network models of cognition: Biobehavioral foundations. Amsterdam: Elsevier Science Press.Google Scholar
  6. Floridi, L. (2004). Open problems in the philosophy of information. Metaphilosophy, 35, 554–582.CrossRefGoogle Scholar
  7. Floridi, L., et al. (2004). The method of abstraction. In M. Negrotti (Ed.), Yearbook of the artificial, dedicated to “models in contemporary sciences” (pp. 177–220). Berna: P. Lang.Google Scholar
  8. Grim, P., et al. (2001). Evolution of communication with a spatialized genetic algorithm. Evolution of Communication, 3, 105–134.CrossRefGoogle Scholar
  9. Harnad, S. (1990). The symbol grounding problem. Physica, D, 335–346.Google Scholar
  10. Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York: John Wiley & Sons.Google Scholar
  11. Menczer, F., et al. (2000). Efficient and scalable pareto optimization by evolutionary local selection algorithms. Evolutionary Computation, 8, 223–247.CrossRefGoogle Scholar
  12. Menczer, F., et al. (2001). Evolving heterogeneous neural agents by local selection. In M. Patel, et al. (Eds.), Advances in the evolutionary synthesis of intelligent agents (pp. 337–366). Cambridge, MA: MIT Press.Google Scholar
  13. Mosses, P. D. (1992). Action semantics. Cambridge University Press.Google Scholar
  14. Pinker, S. (1994). The language instinct. New York: William Morrow.Google Scholar
  15. Real, L. A. (1991). Animal choice behavior and the evolution of cognitive architecture. Science, 30, 980–985.CrossRefGoogle Scholar
  16. Smith, J. M., et al. (1999). The origins of life. Oxford University Press.Google Scholar
  17. Steels, L. (2005). The emergence and evolution of linguistic structure: from lexical to grammatical communication systems. Connection Science, 17, 213–230.CrossRefGoogle Scholar
  18. Taddeo, M., et al. (2005). Solving the symbol grounding problem: A critical review of fifteen years of research. Journal of Experimental and Theoretical Artificial Intelligence, 17, 419–445.CrossRefGoogle Scholar
  19. Varshavskaya, P. (2002). Behavior-based early language development on a humanoid robot. In C. G. Prince, et al. (Eds.), Second international workshop on epigenetic robotics: Modelling cognitive development in robotic systems (pp. 149–158). Edinburgh, Scotland.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Dipartimento di Filosofia, Facoltà di Lettere e FilosofiaUniversità degli Studi di Padova PadovaItaly
  2. 2.IEGUniversity of OxfordOxfordGreat Britain
  3. 3.Department of PhilosophyUniversity of HertfordshireHatfieldGreat Britain
  4. 4.St Cross CollegeUniversity of OxfordOxfordGreat Britain

Personalised recommendations