Advertisement

AI & Society

, Volume 9, Issue 2–3, pp 116–137 | Cite as

Simulating conversations: The communion game

  • Stephen J. Cowley
  • Karl MacDorman
Article

Abstract

In their enthusiasm for programming, computational linguists have tended to lose sight of what humansdo. They have conceived of conversations as independent of sound and the bodies that produce it. Thus, implicit in their simulations is the assumption that the text is the essence of talk. In fact, unlike electronic mail, conversations are acoustic events. During everyday talk, human understanding depends both on the words spoken and on fine interpersonal vocal coordination. When utterances are analysed into sequences of word-based forms, however, these prosodic aspects of language disappear. Therefore, to investigate the possibility that machines might talk, we propose acommunion game that includes this interpersonal patterning. Humans and machines would talk together and, based on recordings of them, a panel would appraise the relevant merit of each machine's simulation by how true to life it sounded. Unlike Turing's imitation game, the communion game overtly focuses attention, not on intelligence, but on language. It is designed to facilitate the development of social groups of adaptive robots that exploit complex acoustic signals in real time. We consider how the development of such machines might be approached.

Key words

Adaptation AI Conversation Corrdination Imitation game Language Machine learning Turing test Simulation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allen, W.S. (1973)Accent and rhythm prosodic, features of latin and Greek: A study in theory and reconstruction. Cambridge: Cambridge University Press.Google Scholar
  2. Alper, G. (1990). A psychoanalyst takes the Turing test.Psychoanalytic Review 77(1), 59–68.Google Scholar
  3. Atkinson, J.M. & Heritage, J., Eds. (1984).Structures of social action: Studies in conversation analysis. Cambridge: Cambridge University Press.Google Scholar
  4. Auer, P., and di Luzio, A., Eds. (1992).The contextualization of language Amsterdam: John Benjamens.Google Scholar
  5. Bateson, G. (1979).Mind and nature: A necessary unity. New York: Ballantine.Google Scholar
  6. Bateson, P.P.G. (1988). Biological evolution of cooperation and trust. In D. Gambetta (Ed.),Trust: Making and breaking of cooperative relations Oxford: Blackwell.Google Scholar
  7. Bieri, P. (1988). Thinking machines: Some reflections on the Turing test.Poetics Today, 9(1), 163–186.CrossRefGoogle Scholar
  8. Block, N. (1978). Troubles with functionalism. In C.W. Savage (Ed.),Perception and cognition: Issues in the foundations of psychology. Minnesota studies in the philosophy of science (Vol. 9, pp. 261–325). Minneapolis: University of Minnesota Press.Google Scholar
  9. Block, N. (1981). Psychologism and behaviorism.The Philosophical Review, 90(1), 5–43.CrossRefGoogle Scholar
  10. Bloomfield, L. (1933).Language. New York: Henry Holt.Google Scholar
  11. Brooks, R.A. (1991a). Intelligence without reason. InIJCAI-91: Proceedings of the Twelfth International Conference on Artificial Intelligence, Sydney, Australia (Vol. 1), pp. 569–595. San Mateo, CA: Morgan Kaufmann.Google Scholar
  12. Brooks, R.A. (1991b). Intelligence without representation.Artificial Intelligence, 47, 139–159.CrossRefGoogle Scholar
  13. Brown, E.D. (1979). The song of the common crow,Corvus brachyrhynchos. Master's thesis at University of Maryland, College Park.Google Scholar
  14. Carling, C. & Moore, T. (1982).Language understanding: Towards a post-Chomskyan linguistics. New York: St. Martin's Press.Google Scholar
  15. Chomsky, N. (1957)Syntactic structures. The Hague: Mouton.Google Scholar
  16. Chomsky, N. (1965).Aspects of the theory of syntax. Cambridge, MA: MIT Press.Google Scholar
  17. Chomsky, N. (1986).Knowledge and language: Its nature, origin, and use. New York: Praeger.Google Scholar
  18. Chomsky, N. (1988).Language and problems of knowledge: The Managua lectures. Cambridge, MA: MIT Press.Google Scholar
  19. Clocksin, W.F., & Moore, A.M. (1989). Experiments in adaptive state-space robotics. InAISB89: Proceedings of the Seventh Conference of the Society for the Study of Artificial Intelligence and Simulation of Behaviour, Brighton, UK, pp. 115–125. San Mateo, CA: Morgan Kaufmann.Google Scholar
  20. Colby, K.M., Hilf, F.D., Weber, S., Kraemer, H.C. (1972). Turing-like indistinguishability tests for the validation of a computer simulation of paranoid processes.Artificial Intelligence, 3, 199–221.CrossRefGoogle Scholar
  21. Cowley, S. J. (1993).The place of prosody in Italian conversations Unpublished doctoral dissertation, University of Cambridge, Cambridge, UK.Google Scholar
  22. Cowley, S.J. (1994). Conversational functions of rhythmical patterning—A behavioural perspective.Language & Communication, 14(4), 353–376.CrossRefGoogle Scholar
  23. Cowley, S.J. (1996). Conversation, coordination, and vertebrate communication.Semiotica.Google Scholar
  24. Crystal, D. (1969).Prosodic systems and intonation in English. Cambridge: Cambridge University Press.Google Scholar
  25. Dreyfus, H.L. (1992).What computers still can't do: A critique of artificial reason. Cambridge, MA: MIT Press.Google Scholar
  26. Farabaugh, S. M. (1982). The ecological and social significance of duetting. In D. Kroodsma, E.H. Miller, and H. Ouelett (Eds.).Acoustic communication in birds: Song learning and its consequences (Vol. 2, pp. 85–124). London: Academic Press.Google Scholar
  27. Fodor, Jerry. (1975)The language of thought. New York: Cromwell.Google Scholar
  28. French, R.M. (1990). Subcognition and the limits of the Turing test.Mind 99(393), 53–65.MathSciNetCrossRefGoogle Scholar
  29. Giles, H. & Coupland, N. (1991).Language: Contexts and consequences. Milton Keynes: Open University Press.Google Scholar
  30. Gödel, K. (1931). Uber formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I.Monatshefte für Mathematik und Physik, 38, 173–198.Google Scholar
  31. Gumperz, J.J. (1982),Discourse strategies. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  32. Gunderson, K. (1971).Mentality and machines New York: Doubleday.Google Scholar
  33. Halliday, M.A.K. (1978). Meaning of the construction of reality in early childhood. In H.L. Pick, Jr., & E. Salzman (Eds.),Modes of perceiving and processing information (pp. 67–96) Hillsdale, NJ: Erbaum.Google Scholar
  34. Halliday, M.A.K. (1979). One child's protolanguage. In M. Bullowa (Ed.),Before speech: The beginning of interpersonal communication (pp. 171–190). London: Cambridge University Press.Google Scholar
  35. Harnad, S. (1989). Minds, machines and Searle.Journal of Experimental and Theoretical Artificial Intelligence, 1, 5–25.CrossRefGoogle Scholar
  36. Harnad, S. (1990). The symbol grounding problem.Physica D, 42, 335–346.CrossRefGoogle Scholar
  37. Harré, R., & Gillett, G. (1994).The discursive mind. Thousand Oaks, CA: Sage Publications.Google Scholar
  38. Harris, R. (1981).The language myth. London: Duckworth.Google Scholar
  39. Harris, R. (1987).The language machine. London: Duckworth.Google Scholar
  40. Hart, J. `t, Collier, R. & Cohen, A. (1990).A perceptual study of intonation: An experimental approach to speech melody. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  41. Hauser, L. (1993). Reaping the whirlwind: Reply to Harnad's “Other Bodies, Other Minds.”Minds and Machines, 3(2), 219–237.MathSciNetCrossRefGoogle Scholar
  42. Hobbs, A.G., Yeomanson, E.W., & Gee, A.C. (1973).Teleprinter Handbook. Hertfordshire, UK: Radio Society of Great Britain.Google Scholar
  43. Hultzén, L.S. (1964). Grammatical intonation. In D. Abercrombie, D.B. Fry, P.A.D. MacCarthy, N.C. Scott, J.L.M. Trim (Eds.),In honour of Daniel Jones: Papers contributed on the occasion of his eightieth birthday (pp. 85–95). London: Longmans.Google Scholar
  44. Huxor, A.P. (1994).Artificial intelligence as writing: Knowledge-based hypertext systems as medium for communication. Unpublished doctoral dissertation, Middlesex University, Middlesex, UK.Google Scholar
  45. Jacquette, D. (1993). A turing test conversation.Philosophy, 68(264), 231–233.CrossRefGoogle Scholar
  46. Karelis, C. (1986). Reflections on the Turing test.Journal for the Theory of Social Behavior, 16(2), 161–172.CrossRefGoogle Scholar
  47. Katz, B. (1990). Using English for indexing and retrieving. In P.H. Winston and S.A. Shellard (Eds.),Artificial Intelligence at MIT: Expanding Frontiers. Cambridge, MA: MIT Press.Google Scholar
  48. Klatt, D.H. (1977). Review of the ARPA speech understanding project.JASA, 62(6) 1345–1366.CrossRefGoogle Scholar
  49. Krebs, J.R., & Dawkins, R. (1984). Animal signals: Mind-reading and manipulation. In J. R. Krebs and N.B. Davies (Eds.),Behavioral ecology: An evolutionary approach (2nd ed., pp. 380–402) Oxford, UK: Blackwell Scientific.Google Scholar
  50. Laver, J. (1980).The phonetic description of voice quality. Cambridge, UK: Cambridge University Press.Google Scholar
  51. Laver, J. (1993).Principles of phonetics. Cambridge, UK: Cambridge University Press.Google Scholar
  52. Levinson, S.C. (1995). Interactional biases in human thinking. In E.N. Goody (Ed.),Social intelligence and interaction. Cambridge: Cambridge University Press.Google Scholar
  53. Lenat, D.B. & Guha, R.V. (1990).Building large knowledge-based systems: Representation and inference in the Cyc project. Reading, MA: Addison-Wesley.Google Scholar
  54. Locke, J. (1993).The child's path to spoken language. Cambridge, MA: Harvard University Press.Google Scholar
  55. Lowerre, T., & Reddy, D.R. (1980). The Harpy speech understanding system. In W.A. Lea (Ed.),Trends in speech recognition (pp. 340–360). Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  56. Lucas, J.R. (1961). Minds, machines and Gödel.Philosophy, 36, 112–127.CrossRefGoogle Scholar
  57. Lyons, J. (1977).Semantics (Vols. 1–2). Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  58. Malinowski, B. (1923). The problem of meaning in primitive languages. In C.K. Ogden and I.A. Richards,The meaning of meaning: A study of the influence of language upon thought and the science of symbolism (pp. 451–510). London: Kegan Paul, Trench, Trubner & Co.Google Scholar
  59. Martin, P.R., & Bateson, P. (1986).Measuring behavior: An introductory guide. Cambridge, UK: Cambridge University Press.Google Scholar
  60. Matthews, P.H. (1993).Grammatical theory in the United States from Bloomfield to Chomsky. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  61. McDermott, D. (1993). Book review: Building large knowledge-based systems: Representation and inference in the Cyc project, D.B. Lenat & R.V. Guha.Artificial Intelligence, 61(1), 53–63.CrossRefGoogle Scholar
  62. Mey, J.L. (1993).Pragmatics. Oxford: Basil Blackwell.Google Scholar
  63. Michie, D. (1993). Turing's test and conscious thought.Artificial Intelligence, 60(1), 1–22.MathSciNetCrossRefGoogle Scholar
  64. Michie, D., & Chambers, R. (1968). Boxes: an experiment in adaptive control. In E. Dale and D. Michie (Eds.),Machine Intelligence (Vol. 2, pp. 137–152). Edinburgh: Oliver & Boyd.Google Scholar
  65. Nilsson, N.J. (1984). Shakey the robot. Technical Note 323, SRI AI Center, Menlo Park, CA.Google Scholar
  66. Ogden, C.K., & Richards, I.A. (1923).The meaning of meaning: A study of the influence of language upon thought and the science of symbolism. London: Kegan Paul, Trench, Trubner & Co.Google Scholar
  67. Omohundro, S.M. (1990). Geometric learning algorithms.Physica D, 42(1–3), 307–321.CrossRefGoogle Scholar
  68. Penrose, R. (1989).The emperor's new mind Oxford, UK: Oxford University Press.Google Scholar
  69. Prevost, S. & Steedman, M. (1994). Specifying intonation from context for speech synthesis.Speech and communication, 15, 139–153.CrossRefGoogle Scholar
  70. Saussure, F. de (1916).Course de linguistique générale. Paris: Payot. English Translation [1959],Course in general linguistics. London: Peter Owen.Google Scholar
  71. Schank, R.G., & Ableson, R.P. (1977).Scripts, Goals, Plans and Understanding. Hillsdale, NJ: Erlbaum.Google Scholar
  72. Searle, J.R. (1980). Minds, brains, and programs.The Behavioral and Brain Sciences, 3, 417–457.CrossRefGoogle Scholar
  73. Shannon, C.E. and Weaver, W. (1949).The mathematical theory of communication. Urbana, IL: University of Illinois Press.zbMATHGoogle Scholar
  74. Shanon, B. (1989). A simple comment regarding the Turing test.Journal for the Theory of Social Behavior 19(2), 249–259.CrossRefGoogle Scholar
  75. Shieber, S.M. (1994). Lessons from a restricted Turing test.Communications of the ACM, 37(6), 70–78.CrossRefGoogle Scholar
  76. Sims, K. (1994). Evolving 3D morphology and behavior by competition.Artificial Life IV Proceedings, pp. 28–39. Cambridge: MIT Press.Google Scholar
  77. Slezak, P. (1982). Gödel's theorem and the mind.British Journal for the Philosophy of Science, 33, 41–52.MathSciNetCrossRefGoogle Scholar
  78. Smolensky, P. (1988). On the proper treatment of connectionismBehavioral and Brain Sciences, 11(1), 1–23.MathSciNetCrossRefGoogle Scholar
  79. Sommerhoff, G., & MacDorman, K.F. (1994). An account of consciousness in physical and functional terms: A target for research in the neurosciences.Integrative Physiological and Behavioral Science, 29(2), 151–181.CrossRefGoogle Scholar
  80. Sperber, D. & Wilson, D. (1986).Relevance: Communication and cognition. Oxford: Basil Blackwell.Google Scholar
  81. Steedman, M. (1991). Structure and intonation.Language, 68, 260–296.Google Scholar
  82. Strawson, P.F. (1950). On referring.Mind, 59, 320–344.CrossRefGoogle Scholar
  83. Sutton, R.S. (1988). Learning to predict by the methods of temporal differences.Machine Learning, 3(1), 9–44.Google Scholar
  84. Thorpe, W.H. (1972). Duetting and antiphonal song in birds: Its extent and significance,Behavior, Suppl.18, pp. 1–197.Google Scholar
  85. Trevarthen, C. (1979). Communication and co-operation in early infancy: A description of primary intersubjectivity. In M. Bullowa (Ed.),Before speech: The beginning of interpersonal communication (pp. 321–348). Cambridge: Cambridge University Press.Google Scholar
  86. Trevarthen, C. (1986). Sharing makes sense: Intersubjectivity and the making of an infant's meaning. In R. Steele and T. Threadgold (Eds.),Language Topics: Essays in honour of M. Halliday, (Vol. 1, pp. 177–200) Amsterdam: J. Benjamens.Google Scholar
  87. Turing, A. (1950). Computing machinery and intelligence.Mind 59, 433–460.MathSciNetCrossRefGoogle Scholar
  88. Üxküll, J.J., Baron von (1921).Umwelt und Innenwelt der Tiere. Berlin: Springer.Google Scholar
  89. Waibel, A. (1988).Prosody and speech recognition. London: Pitman.zbMATHGoogle Scholar
  90. Watkins, C.J.C.H., & Dayan, P. (1992). Q-learning.Machine Learning, 8(3–4), 279–292.zbMATHGoogle Scholar
  91. Weizenbaum, J. (1965). ELIZA—a computer program for the study of natural language communication between man and machine.Communications of the Association for Computing Machinery, 9, 36–45.CrossRefGoogle Scholar
  92. Weizenbaum, J. (1976).Computing power and human reason: From judgment to calculation. San Francisco: W. H. Freeman.Google Scholar
  93. Wheddon, C., & Linggard, R. (1990).Speech and language processing. London: Chapman & Hall.Google Scholar
  94. Wilensky, R. (1983).Planning and understanding: A computational approach to human reasoning. Reading, MA: Addison-Wesley.Google Scholar
  95. Winograd, T. & Flores, F. (1986).Understanding computers and cognition: A new foundation for design. Norwood, NJ: Ablex.zbMATHGoogle Scholar
  96. Winograd, T. (1972).Understanding natural language New York: Academic Press.Google Scholar
  97. Wittgenstein, L. (1958).Philosophical investigations. Oxford, UK: Basil Blackwell.Google Scholar

Copyright information

© Springer-Verlag London Limited 1995

Authors and Affiliations

  • Stephen J. Cowley
    • 1
  • Karl MacDorman
    • 2
  1. 1.Department of LinguisticsCambridge UniversityCambridgeUK
  2. 2.Computer LaboratoryCambridge UniversityCambridgeUK

Personalised recommendations