Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz)

KI 2015: Advances in Artificial Intelligence pp 256-263 | Cite as

A Critical Review on the Symbol Grounding Problem as an Issue of Autonomous Agents

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9324)

Abstract

Many recent papers claim, that the symbol grounding problem (SGP) remains unsolved. Most AI researchers ignore that and the autonomous agents (or robots) they design indeed do not seem to have any “problem”. Anyway, these claims should be taken rationally, since nearly all these papers make “robots” a subject of the discussion - leaving some kind of impression that what many roboticists do in the long run has to fail because of the SGP not yet being solved. Starting from Searle’s chinese room argument (CRA) and Harnad’s reformulation of the problem, we take a look on proposed solutions and the concretization of the problem by Taddeo’s and Floridi’s “Z condition”. We then refer to two works, which have recently shown that the Z-conditioned SGP is unsolvable. We conclude, that the original, hard SGP is not relevant in the context of designing goal-directed autonomous agents.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bielecka, K.: Why taddeo and floridi did not solve the symbol grounding problem. Journal of Experimental & Theoretical Artificial Intelligence, 1–15 (2014)Google Scholar
  2. 2.
    Brentano, F.: Psychology from an Empirical Standpoint (Psychologie vom empirischen Standpunkt). Linda L. McAlister. Trans. Antos C. Rancurello, DB Terrell, and Linda L. McAlister. Repr. New York: Routledge & Kegan Paul (1874)Google Scholar
  3. 3.
    Bringsjord, S.: The symbol grounding problem remains unsolved. Journal of Experimental & Theoretical Artificial Intelligence, 1–10 (2014)Google Scholar
  4. 4.
    Bringsjord, S., Noel, R.: Real robots and the missing thought experiment in the chinese room dialectic (2002)Google Scholar
  5. 5.
    Brooks, R.A.: Elephants don’t play chess. Robotics and Autonomous Systems 6(1), 3–15 (1990)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Brooks, R.A.: Intelligence without representation. Artificial Intelligence 47(1), 139–159 (1991)CrossRefGoogle Scholar
  7. 7.
    Cangelosi, A., Greco, A., Harnad, S.: Symbol grounding and the symbolic theft hypothesis. In: Simulating the Evolution of Language, pp. 191–210. Springer (2002)Google Scholar
  8. 8.
    Chalmers, D.J.: The conscious mind: In search of a fundamental theory. Oxford University Press (1997)Google Scholar
  9. 9.
    Chella, A., Coradeschi, S., Frixione, M., Saffiotti, A.: Perceptual anchoring via conceptual spaces. In: Proceedings of the AAAI-04 Workshop on Anchoring Symbols to Sensor Data, AAAI. AAAI Press (2004)Google Scholar
  10. 10.
    Cole, D.: Thought and thought experiments. Philosophical Studies 45(3), 431–444 (1984)CrossRefGoogle Scholar
  11. 11.
    Coradeschi, S., Saffiotti, A.: An introduction to the anchoring problem. Robotics and Autonomous Systems 43(2–3), 85–96 (2003)CrossRefGoogle Scholar
  12. 12.
    Cubek, R., Ertel, W., Palm, G.: High-level learning from demonstration with conceptual spaces and subspace clustering. In: Proceedings of the IEEE Int. Conference on Robotics and Automation (ICRA) (2015)Google Scholar
  13. 13.
    Damper, R.: The chinese room argumentdead but not yet buried. Journal of Consciousness Studies 11(5–6), 159–169 (2004)Google Scholar
  14. 14.
    Davidsson, P.: Toward a general solution to the symbol grounding problem: combining machine learning and computer vision. In: AAAI Fall Symposium Series, Machine Learning in Computer Vision: What, Why and How, pp. 157–161 (1993)Google Scholar
  15. 15.
    Fichtner, M.: Anchoring symbols to percepts in the fluent calculus. KI-Künstliche Intelligenz 25(1), 77–80 (2011)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Fields, C.: Equivalence of the symbol grounding and quantum system identification problems. Information 5(1), 172–189 (2014)CrossRefGoogle Scholar
  17. 17.
    Harnad, S.: The symbol grounding problem. Physica D: Nonlinear Phenomena 42, 335–346 (1990)CrossRefGoogle Scholar
  18. 18.
    Harnad, S.: Mind, machines and searle ii: What’s wrong and right about searle’s chinese room argument? (2001)Google Scholar
  19. 19.
    Harnad, S., Scherzer, P.: First, scale up to the robotic turing test, then worry about feeling. Artificial Intelligence in Medicine 44(2), 83–89 (2008)CrossRefGoogle Scholar
  20. 20.
    van Hateren, J.: How the symbol grounding of living organisms can be realized in artificial agents. arXiv preprint arXiv:1503.04941 (2015)
  21. 21.
    Jahren, N.: Can semantics be syntactic? In: Epistemology and Cognition, pp. 155–174. Springer (1991)Google Scholar
  22. 22.
    Mayo, M.J.: 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
  23. 23.
    Melnyk, A.: Searle’s abstract argument against strong ai. Synthese 108(3), 391–419 (1996)MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Moor, J.H.: The pseudorealization fallacy and the chinese room argument. In: Aspects of Artificial Intelligence, pp. 35–53. Springer (1988)Google Scholar
  25. 25.
    Müller, V.C.: Which symbol grounding problem should we try to solve? Journal of Experimental & Theoretical Artificial Intelligence (ahead-of-print), 1–6 (2014)Google Scholar
  26. 26.
    Ogden, C.K., Richards, I.A., Malinowski, B., Crookshank, F.G.: The meaning of meaning. Harcourt, Brace & World New York (1946)Google Scholar
  27. 27.
    Putnam, H.: Meaning and reference. The Journal of Philosophy, 699–711 (1973)Google Scholar
  28. 28.
    Rosenstein, M.T., Cohen, P.R.: Continuous categories for a mobile robot. In: AAAI/IAAI, pp. 634–640 (1999)Google Scholar
  29. 29.
    Searle, J.R.: Minds, brains, and programs. Behavioral and Brain Sciences 3(03), 417–424 (1980)CrossRefGoogle Scholar
  30. 30.
    Searle, J.R.: The failures of computationalism. Think (Tilburg, The Netherlands: Tilburg University Institute for Language Technology and Artificial Intelligence) 2, 68–71 (1993)Google Scholar
  31. 31.
    Steels, L.: The symbol grounding problem has been solved. so whats next. Symbols and embodiment: Debates on meaning and cognition, pp. 223–244 (2008)Google Scholar
  32. 32.
    Steels, L., Belpaeme, T., et al.: Coordinating perceptually grounded categories through language: A case study for colour. Behavioral and Brain Sciences 28(4), 469–488 (2005)Google Scholar
  33. 33.
    Steels, L., Kaplan, F.: Situated grounded word semantics. In: IJCAI, pp. 862–867 (1999)Google Scholar
  34. 34.
    Steels, L., Vogt, P.: Grounding adaptive language games in robotic agents. In: Proceedings of the Fourth European Conference on Artificial Life, vol. 97 (1997)Google Scholar
  35. 35.
    Sun, R.: Symbol grounding: a new look at an old idea. Philosophical Psychology 13(2), 149–172 (2000)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Taddeo, M., Floridi, L.: Solving the symbol grounding problem: a critical review of fifteen years of research. Journal of Experimental & Theoretical Artificial Intelligence 17(4), 419–445 (2005)CrossRefGoogle Scholar
  37. 37.
    Taddeo, M., Floridi, L.: A praxical solution of the symbol grounding problem. Minds and Machines 17(4), 369–389 (2007)CrossRefGoogle Scholar
  38. 38.
    Tellex, S., Kollar, T., Dickerson, S., Walter, M.R., Banerjee, A.G., Teller, S., Roy, N.: Approaching the symbol grounding problem with probabilistic graphical models. AI Magazine 32(4), 64–76 (2011)Google Scholar
  39. 39.
    Tenorth, M., Beetz, M.: Knowrob - knowledge processing for autonomous personal robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 4261–4266. IEEE (2009)Google Scholar
  40. 40.
    Wittgenstein, L., Anscombe, G.E.M.: Philosophische Untersuchungen - Philosophical Investigations. Blackwell Oxford (1953)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute for Artificial IntelligenceRavensburg-Weingarten University of Applied SciencesWeingartenGermany
  2. 2.Institute of Neural Information ProcessingUlm UniversityUlmGermany

Personalised recommendations