Advertisement

Complex Systems and the Evolution of Artificial Intelligence

  • Klaus Mainzer
Chapter
  • 160 Downloads

Abstract

Can machines think? This famous question from Turing has new topicality in the framework of complex systems. The chapter starts with a short history of computer science since Leibniz and his program for mechanizing thinking (mathesis universalis) (Sect. 5.1). The modern theory of computability enables us to distinguish complexity classes of problems, meaning the order of corresponding functions describing the computational time of their algorithms or computational programs. Modern computer science is interested not only in the complexity of universal problem solving but also in the complexity of knowledge-based programs. Famous examples are expert systems simulating the problem solving behavior of human experts in their specialized fields. Further on, we ask if a higher efficiency of problem solving may be expected from quantum computers and quantum complexity theory (Sect. 5.2).

Keywords

Expert System Cellular Automaton Turing Machine Computer Virus Certainty Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 5.1
    For this chapter compare Mainzer, K.: Computer - Neue Flügel des Geistes? De Gruyter: Berlin/New York (1993).Google Scholar
  2. Mainzer, K.: Die Evolution intelligenter Systeme. Zeitschrift für Semiotik 12 (1990) 81–104Google Scholar
  3. 5.2
    Feigenbaum, E.A., McCorduck, P.: The Fifth Generation. Artificial Intelligence and Japan’s Computer Challenge to the World. Michael Joseph: London (1984)Google Scholar
  4. 5.3
    Cf. Williams, M.R.: A History of Computing Technology. Prentice-Hall: Engle- wood Cliffs (1985)Google Scholar
  5. 5.4
    Cohors-Fresenborg, E.: Mathematik mit Kalkülen und Maschinen. Vieweg: Braunschweig (1977) 7zbMATHCrossRefGoogle Scholar
  6. 5.
    Herrn von Leibniz’ Rechnung mit Null und Eins. Siemens AG: Berlin (1966).Google Scholar
  7. Mackensen, L. von: Leibniz als Ahnherr der Kybernetik–ein bisher unbekannter Leibnizscher Vorschlag einer “Machina arithmetica dyadicae”. In: Akten des II. Internationalen Leibniz-Kongresses 1972 Bd. 2. Steiner: Wiesbaden (1974) 255–268Google Scholar
  8. 5.6
    Scholz, H.: Mathesis Universalis. Schwabe: Basel (1961)zbMATHGoogle Scholar
  9. 5.7
    Babbage, C.: Passages from the Life of a Philosopher. Longman and Co.: London (1864).Google Scholar
  10. Bromley, A.G.: Charles Babbage’s Analytical Engine 1838. Annals of the History of Computing 4 (1982) 196–219MathSciNetzbMATHCrossRefGoogle Scholar
  11. 5.8
    Cf. Minsky, M.: Recursive unsolvability of Post’s problem of “tag” and other topics in the theory of Turing machines. Annals of Math. 74, 437–454.Google Scholar
  12. Sheperdson, J.C., Sturgis, H.E.: Computability of recursive functions. J. Assoc. Comp. Mach. 10 (1963) 217–255.CrossRefGoogle Scholar
  13. Sheperdson, J.C., Sturgis, H.E.: For the following description of register machines compare Rödding, D.: Klassen rekursiver Funktionen. In: Löb, M.H. (ed.): Proceedings of the Summer School in Logic. Springer: Berlin (1968) 159–222.Google Scholar
  14. CohorsFresenborg, E.: Mathematik mit Kalkülen und Maschinen (see Note 4)Google Scholar
  15. 5.9
    Turing, A.M.: On computable numbers, with an application to the “Entscheidungsproblem”. Proc. London Math. Soc., Ser. 242 (1936) 230–265.Google Scholar
  16. Post, E.L.: Finite combinatory processes — Formulation I. Symbolic Logic I (1936) 103–105.Google Scholar
  17. Davis, M.: Computability and Unsolvability. McGraw-Hill: New York (1958) 3zbMATHGoogle Scholar
  18. 5.10
    Arbib, M.A.: Brains, Machines, and Mathematics. Springer: New York (1987) 131zbMATHCrossRefGoogle Scholar
  19. 5.11
    Mainzer, K.: Der Konstruktionsbegriff in der Mathematik. Philosophia Naturalis 12 (1970) 367–412MathSciNetGoogle Scholar
  20. 5.12
    Cf. Knuth, D.M.: The Art of Computer Programming. Vol. 2. Addison-Wesley: Reading, MA (1981).Google Scholar
  21. Börger, E.: Berechenbarkeit, Komplexität, Logik. Vieweg: Braunschweig (1985)zbMATHGoogle Scholar
  22. 5.13
    Grötschel, M., Lovâsc, L., Schryver, A.: Geometric Algorithms and Combinatorial Optimization. Springer: Berlin (1988)zbMATHCrossRefGoogle Scholar
  23. 5.14
    Gardner, A.: Penrose Tiles to Trapdoor Ciphers. W.H. Freeman: New York (1989)Google Scholar
  24. 5.15
    Cf. Arbib, M.A.: Speed-up theorems and incompleteness theorem. In: Caianiello, E.R. (ed.): Automata Theory. Academic Press (1966) 6–24.Google Scholar
  25. Mostowski, A.: Sentences Undecidable in Formalized Arithmetic. North-Holland: Amsterdam (1957).Google Scholar
  26. Beth, E.W.: The Foundations of Mathematics, North-Holland: Amsterdam (1959).zbMATHGoogle Scholar
  27. Kleene, S.C.: Introduction to Metamathematics. North-Holland: Amsterdam (1967)Google Scholar
  28. 5.16
    Mainzer, K.: Rationale Heuristik und Problem Solving. In: Burrichter, C., Inhetveen, R., Kötter, R. (eds.): Technische Rationalität und rationale Heuristik. Schöningh: Paderborn (1986) 83–97Google Scholar
  29. 5.17
    Mainzer, K.: Knowledge-based systems. Remarks on the philosophy of technology and Artificial Intelligence. Journal for General Philosophy of Science 21 (1990) 47–74Google Scholar
  30. 5.18
    Bibel, W., Siekmann, J.: Künstliche Intelligenz. Springer: Berlin (1982).CrossRefGoogle Scholar
  31. Nilson, N.J.: Principles of Artificial Intelligence. Springer: Berlin (1982).Google Scholar
  32. Kredel, L.: Künstliche Intelligenz und Expertensysteme. Droemer Knaur: München (1988)Google Scholar
  33. 5.19
    Puppe, F.: Expertensysteme. Informatik-Spektrum 9 (1986) 1–13MathSciNetGoogle Scholar
  34. 5.20
    For instance, one of the constructors of DENDRAL and MYCIN received a Ph.D. in philosophy for his dissertation “Scientific Discoveries”, supervised by Gerald Massey (today Center for Philosophy of Science, Pittsburgh). Also refer to Glymour, C.: AI is philosophy. In: Fetzer, J.H. (ed.): Aspects in Artificial Intelligence. Kluwer Academic Publisher: Boston (1988) 195–207Google Scholar
  35. 5.21
    Buchanan, B.G., Sutherland, G.L., Feigenbaum, E.A.: Heuristic DENDRAL: A program for generating processes in organic chemistry. In: Meltzer, B., Michie, D. (eds.): Machine Intelligence 4. Edinburgh (1969).Google Scholar
  36. Buchanan, B.G., Feigenbaum, E.A.: DENDRAL and META-DENDRAL: Their applications dimension. In: Artificial Intelligence 11 (1978) 5–24Google Scholar
  37. 5.22
    McCarthy, J.: LISP Programmer’s Manual (Rep. MIT Press ). Cambridge, MA (1962).Google Scholar
  38. Stoyan, H., Goerz, G.: LISP — Eine Einführung in die Programmierung. Springer: Berlin (1984)zbMATHGoogle Scholar
  39. 5.23
    Randall, D., Buchanan, B.G., Shortlife, E.H.: Production rules as a representation for a knowledge-based consultation program. Artificial Intelligence 8 (1977).Google Scholar
  40. Shortliffe, E.H.: MYCIN: A rule-based computer program for advising physicians regarding antimicrobial therapy selection. AI Laboratory, Memo 251, STANCS-74–465, Stanford University.Google Scholar
  41. Winston, P.H.: Artificial Intelligence. Addison-Wesley: Reading, MA (1977) Chap. 9Google Scholar
  42. 5.24
    Doyle, J.: A truth maintenance system. Artificial Intelligence 12 (1979) 231–272MathSciNetCrossRefGoogle Scholar
  43. 5.25
    Lenat, D.B., Harris, G.: Designing a rule system that searches for scientific discoveries. In: Waterman, D.A., Hayes-Roth, F. (eds.): Pattern Directed Inference Systems. New York (1978) 26.Google Scholar
  44. Lenat, D.B.: AM: Discovery in mathematics as heuristic search. In: Davis, R., Lenat, D.B. (eds.): Knowledge-Based Systems in Artificial Intelligence. New York (1982) 1–225Google Scholar
  45. 5.26
    Langley, P.: Data-driven discovery on physical laws. Cognitive Science 5 (1981) 31–54CrossRefGoogle Scholar
  46. 5.27
    Langley, P., Simon, H.A., Bradshaw, G.L., Zytkow, J.M.: Scientific Discovery: Computational Explorations of the Creative Processes. Cambridge, MA (1987)Google Scholar
  47. 5.28
    Kulkarni, D., Simon, H.A.: The process of scientific discovery: The strategy of experimentation. Cognitive Science 12 (1988) 139–175CrossRefGoogle Scholar
  48. 5.
    Cf. Winston, P.H.: Artificial Intelligence (see Note 23)Google Scholar
  49. 5.30
    Audretsch, J., Mainzer, K. (eds.): Wieviele Leben hat Schrödingers Katze? B.I. Wissenschaftsverlag: Mannheim (1990) 273 (Fig. 5.14a), 274 (Fig. 5.14.b), 276 (Fig. 5. 14c )Google Scholar
  50. 5.31
    Penrose, R.: Newton, quantum theory and reality. In: Hawking, S.W., Israel, W: 300 Years of Gravity. Cambridge University Press: Cambridge (1987)Google Scholar
  51. 5.32
    Deutsch, D.: Quantum theory, the Church-Turing principle and the universal quantum computer. Proc. Roy. Soc. A400 (1985) 97–117MathSciNetADSzbMATHCrossRefGoogle Scholar
  52. 5.33
    For this chapter compare Mainzer, K.: Computer — Neue Flügel des Geistes? De Gruyter: Berlin (1993).Google Scholar
  53. Mainzer, K.: Philosophical concepts of computational neuroscience. In: Eckmiller, R., Hartmann, G., Hauske, G. (eds.): Parallel Processing in Neural Systems and Computers. North-Holland: Amsterdam (1990) 9–12Google Scholar
  54. 5.
    McCullock, W.S., Pitts, W: A logical calculus of the ideas immanent in nervous activity. In: Bulletin of Mathematical Biophysics 5 (1943) 115–133.Google Scholar
  55. Arbib, M.A.: Brains, Machines and Mathematics (see Note 10 ) 18Google Scholar
  56. 5.35
    Neumann, J. von: The Computer and the Brain. Yale University Press: New Haven (1958)zbMATHGoogle Scholar
  57. 5.36
    Cf. Burks, A.W. (ed.): Essays on Cellular Automata. University of Illinois Press (1970).Google Scholar
  58. Myhill, J.: The abstract theory of self-reproduction. In: Mesarovic, M.D. (ed.): Views on General Systems Theory. John Wiley (1964) 106–118Google Scholar
  59. 5.37
    Wolfram, S.: Universality and complexity in cellular automata. In: Farmer, D., Toffoli, T., Wolfram, S. (eds.): Cellular Automata. Proceedings of an Interdisciplinary Workshop. North-Holland: Amsterdam (1984) 1–35.Google Scholar
  60. Wolfram, S.: Theory and Applications of Cellular Automata. World Scientific: Singapore (1986).zbMATHGoogle Scholar
  61. Demongeot, J., Golés, E., Tchuente, M. (eds.): Dynamical Systems and Cellular Automata. Academic Press: London (1985).Google Scholar
  62. Poundstone, W: The Recursive Universe. Cosmic Complexity and the Limits of Scientific Knowledge. Oxford University Press: Oxford (1985)Google Scholar
  63. 5.38
    Rosenblatt, E: The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65 (1958) 386–408MathSciNetCrossRefGoogle Scholar
  64. 5.39
    Minsky, M., Papert, S.A.: Perceptrons. MIT Press: Cambridge MA (1969) (expanded edition 1988 )Google Scholar
  65. 5.40
    Gorman, R.P., Sejnowski, T.J.: Analysis of hidden units in a layered network trained to classify sonar targets. Neural Networks 1 (1988) 75–89.CrossRefGoogle Scholar
  66. Churchland, P.M.: A Neurocomputational Perspective. The Nature of Mind and the Structure of Science. MIT Press: Cambridge MA (1989)Google Scholar
  67. 5.41
    Sejnowski, T.J., Rosenberg, C.R.: NETtalk: A parallel network that learns to read aloud. The Johns Hopkins University Electrical Engineering and Computer Science Technical Report IHU/EECS-86/01 (1986) 32.Google Scholar
  68. Cf. Kinzel, W/Deker, U.: Der ganz andere Computer: Denken nach Menschenart. Bild der Wissenschaft 1 (1988) 43Google Scholar
  69. 5.42
    Schöneburg, E., Hansen, N., Gawelczyk: Neuronale Netzwerke. Markt and Technik: München (1990) 176, 177Google Scholar
  70. 5.43
    Rumelhart, D.E., Smolensky, P., McClelland, J.L., Hinton, G.E.: Schemata and sequential thought processes. In: McClelland, J.L., Rumelhart, D.E. (eds.): Parallel Distributed Processing: Explorations in the Microstructure of Cognition vol. 2: Applications. MIT Press: Cambridge MA (1986).Google Scholar
  71. Churchland, P.S.: Neurophilosophy. Toward a Unified Science of the Mind-Brain. MIT Press: Cambridge MA (1988) 465Google Scholar
  72. 5.44
    Haken, H. (ed.): Neural and Synergetic Computers. Springer: Berlin (1988) 5–7zbMATHGoogle Scholar
  73. 5.45
    Haken, H.: Synergetics as a tool for the conceptualization and mathematization of cognition and behaviour — How far can we go? In: Haken, H., Stadler, M. (eds.): Synergetics of Cognition. Springer: Berlin (1990) 23–24CrossRefGoogle Scholar
  74. 5.46
    For chapter 5.4 compare Mainzer, K.: Philosophical foundations of neurobionics. In: Bothe, H.W., Samii, M., Eckmiller, R. (eds.): Neurobionics. An Interdisciplinary Approach to Substitute Impaired Functions of the Human Nervous System. North-Holland: Amsterdam (1993) 31–47Google Scholar
  75. 5.47
    Cf. Samii, M. (ed.): Peripheral Nerve Lesions. Springer: Berlin (1990)Google Scholar
  76. 5.48
    Eckmiller, R. (ed.): Neurotechnologie-Report. Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie: Bonn (1995).Google Scholar
  77. Stein, R.B., Peckham, P.H. (eds.): Neuroprostheses: Replacing Motor Function After Disease or Disability. Oxford University Press: New York (1991)Google Scholar
  78. 5.49
    Cosman, E.R.: Computer-assisted technologies for neurosurgical procedures. In: Bothe, H.W., Samii, M., Eckmiller, R. (eds.): Neurobionics (see Note 46 ) 365Google Scholar
  79. 5.50
    Foley, J.D., Dam, A. van: Fundamentals of Interactive Computer Graphics. Addison-Wesley: Amsterdam (1982).Google Scholar
  80. Foley, J.D.: Neuartige Schnittstellen zwischen Mensch und Computer. Spektrum des Wissenschaft 12 (1987)Google Scholar
  81. 5.51
    Gibson, W.: Neuromancer. Grafton: London (1986) 67Google Scholar
  82. 5.52
    Putnam, H.: Reason, Truth and History. Cambridge University Press: Cambridge (1981) 6CrossRefGoogle Scholar
  83. 5.53
    Langton, C.G. (ed.): Artificial Life, Addison Wesley: Redwood City (1989)Google Scholar
  84. 5.54
    Lindenmayer, A., Rozenberg, G. (eds.): Automata, Languages, Development. North-Holland: Amsterdam (1976).Google Scholar
  85. Lindenmayer, A.: Models for multicellular development: Characterization, inference and complexity of L-systems. In: Kelemenovâ, A., Kelemen, J. (eds.): Trends, Techniques and Problems in Theoretical Computer Science, Springer: Berlin (1987) 138–168CrossRefGoogle Scholar
  86. 5.55
    Langton, C.G.: Self-reproduction in cellular automata. Physica D 10 (1984) 135–144.ADSCrossRefGoogle Scholar
  87. Artificial Life, in: Langton [5.53] Plates 1–8Google Scholar
  88. 5.56
    Langton, C.G.: Life at the edge of chaos. In: Langton, C.G., Taylor, C., Farmer, J. D., Rasmussen, S. (eds.): Artificial Life II, Addison-Wesley: Redwood City (1992) 41–91Google Scholar
  89. 5.57
    Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzi- pien der biologischen Evolution, Frommann-Holzboog: Stuttgart (1973)Google Scholar
  90. 5.58
    Holland, J.H.: Adaption in Natural and Artificial Systems. The MIT Press: Cambridge, MA (1992, 1st ed. 1975 )Google Scholar
  91. 5.59
    Mitchell, M., Forrest, S.: Genetic Algorithms and Artificial Life. Artificial Life 1 (1994) 267–289CrossRefGoogle Scholar
  92. 5.60
    Forrest, S., Javornik, B., Smith, R., Perelson, A.: Using genetic algorithms to explore pattern recognition in the immune system. Evolutionary Computation 1 (1993) 191–211CrossRefGoogle Scholar
  93. 5.61
    Highland, H.J. (ed.): Computer Virus Handbook. Elsevier Advanced Technology: Oxford (1990).Google Scholar
  94. Spafford, E.H.: Computer viruses as artificial life. Artificial Life 1 (1994) 249–265CrossRefGoogle Scholar
  95. 5.62
    Taylor, C., Jefferson, D.: Artificial life as a tool for biology inquiry. Artificial Life 1 (1994) 1–13CrossRefGoogle Scholar
  96. 5.63
    Moran, F., Moreno, A., Merelo, J.J., Chacön, P. (eds.): Advances in Artificial Life, Springer: Berlin (1995)zbMATHGoogle Scholar
  97. 5.
    Mainzer, K.: Gehirn.Google Scholar
  98. Computer, Komplexität. Springer: Berlin 1997Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Klaus Mainzer
    • 1
  1. 1.Lehrstuhl für Philosophie und WissenschaftstheorieUniversität AugsburgAugsburgGermany

Personalised recommendations

Cite chapter

Buy options