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Multidisciplinary Trends in Modern Artificial Intelligence: Turing’s Way

Part of the Studies in Computational Intelligence book series (SCI, volume 427)

Abstract

The paper faces the challenge to generalize existing trends and approaches in the field of artificial intelligence. Under consideration are expert systems, dynamic neural networks, probabilistic reasoning, fuzzy logic, genetic algorithms, multi-agent systems, bio-inspired algorithms, distributed nonlinear computing, chaos-driven pattern recognition. Each approach strengths and limitations are stated without exhaustive treatment to involve specialist from adjacent fields in discussion. The most perspective research directions are revealed and analyzed in reference to Turing’s way in artificial intelligence and beyond.

Keywords

artificial intelligence multidisciplinarity bio-inspired methods chaotic neural network Turing machine self-organization chaotic maps chaotic computing 

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References

  1. 1.
    Aaronson, S.: The Limits of Quantum Computers. Scientific American 298/3(50-7), 36–8733 (2008)Google Scholar
  2. 2.
    Angelini, L., Carlo, F., Marangi, C., Pellicoro, M., Nardullia, M., Stramaglia, S.: Clustering data by inhomogeneous chaotic map lattices. Phys. Rev. Lett. (85), 78–102 (2000)CrossRefGoogle Scholar
  3. 3.
    Arbib, M.: Turing Machines, Finite Automata and Neural Nets. Journal of the ACM 8, 467–475 (1961)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Baum, S.D., Goertzel, B., Goertzel, T.: How long until human-level AI? Results from an expert assessment. Technological Forecasting & Social Change 78, 185–195 (2011)CrossRefGoogle Scholar
  5. 5.
    Benderskaya, E.N., Zhukova, S.V.: Clustering by Chaotic Neural Networks with Mean Field Calculated Via Delaunay Triangulation. In: Corchado, E., Abraham, A., Pedrycz, W. (eds.) HAIS 2008. LNCS (LNAI), vol. 5271, pp. 408–416. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Benderskaya, E.N., Zhukova, S.V.: Fragmentary Synchronization in Chaotic Neural Network and Data Mining. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS, vol. 5572, pp. 319–326. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Benderskaya, E.N., Zhukova, S.V.: Dynamic Data Mining: Synergy of Bio-Inspired Clustering Methods. In: Funatsu, K. (ed.) Knowledge-Oriented Applications in Data Mining, pp. 398–410. InTech (2011) ISBN: 978-953-307-154-1 Google Scholar
  8. 8.
    Benderskaya, E.N., Zhukova, S.V.: Self-organized Clustering and Classification: A Unified Approach via Distributed Chaotic Computing. In: Abraham, A., Corchado, J.M., González, S.R., De Paz Santana, J.F. (eds.) International Symposium on Distributed Computing and Artificial Intelligence. AISC, vol. 91, pp. 423–431. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Benderskaya, E.N., Zhukova, S.V.: Oscillatory Chaotic Neural Network as a Hybrid System for Pattern Recognition. In: Proceedings of IEEE Workshop on Hybrid Intelligent Models and Applications, Paris, France, April 10-15, pp. 39–45 (2011)Google Scholar
  10. 10.
    Benderskaya, E.N., Zhukova, S.V.: Chaotic Clustering: Fragmentary Synchronization of Fractal Waves. In: Esteban, T.-C. (ed.) Chaotic Systems, pp. 187–202. InTech (2011) ISBN: 978-953-307-564-8Google Scholar
  11. 11.
    Blum, C., Merkle, D.: Swarm Intelligence: Introduction and Applications. Springer (2009) ISBN 978-3642093432Google Scholar
  12. 12.
    Bobrow, D.G., Brady, M.: Artificial Intelligence 40 years later. Artificial Intelligence 103, 1–4 (1998)CrossRefGoogle Scholar
  13. 13.
    Borisyuk, R.M., Borisyuk, G.N., Kazanovich, Y.B.: The synchronization principle in modelling of binding and attention. Membrane & Cell Biology 11(6), 753–761 (1998)Google Scholar
  14. 14.
    Boryczka, U.: Finding groups in data: Cluster analysis with ants. Applied Soft Computing (9), 61–70 (2009)Google Scholar
  15. 15.
    Chinchuluun, A., Pardalos, M.P., Migdalas, A., Pitsoulis, L.: Pareto Optimality. Game Theory and Equilibria. In: SOIA, Springer (2008)Google Scholar
  16. 16.
    Cooper, S.B.: Emergence as a computability-theoretic phenomenon. Applied Mathematics and Computation 215, 1351–1360 (2009)MathSciNetMATHCrossRefGoogle Scholar
  17. 17.
    Cristianini, N.: Are we still there? Neural Networks 23, 466–470 (2010)CrossRefGoogle Scholar
  18. 18.
    Delvenne, J.: What is a universal computing machine? Applied Mathematics and Computation 215, 1368–1374 (2009)MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    Diller, A.: Why AI and Robotics are going nowhere fast? In: Vallverdu, J. (ed.) Thinking Machines and the Philosophy of Computer Science: Concepts and Principles, pp. 328–343, Information Science Reference (2010)Google Scholar
  20. 20.
    Dimitriadou, E., Weingessel, A., Hornik, K.: Voting-Merging: An Ensemble Method for Clustering. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 217–224. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  21. 21.
    Giarratano, J.C., Riley, G.D.: Expert Systems. Principles and Programming. Course Technology (2004)Google Scholar
  22. 22.
    Haken, H.: Synergetic Computers and Cognition: A Top-Down Approach to Neural Nets. Springer, SSS (2010)Google Scholar
  23. 23.
    Haken, H.S.: Introduction and Advanced Topics. In: Physics and Astronomy Online Library, p. 758. Springer (2004)Google Scholar
  24. 24.
    Handl, J., Meyer, B.: Ant-based and swarm-based clustering. Swarm Intelligence 1(2), 95–113 (2007)CrossRefGoogle Scholar
  25. 25.
    Haykin, S.: Neural Networks. A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River (1998)Google Scholar
  26. 26.
    Hjelmfelt, A., Weinberger, E.D., Ross, J.: Chemical implementation of neural networks and Turing machines. Proceedings of the National Academy of Sciences of the United States of America 88, 10983–10987 (1991)MATHCrossRefGoogle Scholar
  27. 27.
    Hutter, M.: Universal Algorithmic Intelligence: A mathematical top-down approach. In: Goertzel, B., Pennachin, C. (eds.) Artificial General Intelligence, pp. 227–290. Springer (2007)Google Scholar
  28. 28.
    Hyötyniemi, H.: Turing Machines are Recurrent Neural Networks. In: Alander, J., Honkela, T., Jakobsson, M. (eds.) Proceedings of STeP 1996, pp. 13–24 (1996)Google Scholar
  29. 29.
    Inoue, M., Kaneko, K.: Dynamics of coupled adaptive elements: Bursting and intermittent oscillations generated by frustration in networks. Physical Review E (81), 026203, 1–14 (2010)Google Scholar
  30. 30.
    Izhikevich, E.M.: Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. MIT Press (2010)Google Scholar
  31. 31.
    Jaeger, H.: Short term memory in echo state networks. GMD Report 152: German National Research Center for Information Technology (2001)Google Scholar
  32. 32.
    Jang, J.R., Sun, C., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall (1997)Google Scholar
  33. 33.
    Kaiser, M.: Brain architecture: a design for natural computation. Philosophical Transactions of the Royal Society A 365(1861), 3033–3045 (2007)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Kamps, M.: Towards Truly Human-Level Intelligence in Artificial Applications. Cognitive Systems Research (2011) doi:10.1016/j.cogsys.2011.01.003Google Scholar
  35. 35.
    Kaneko, K.: Chaotic but regular posi-nega switch among coded attractors by cluster-size variations. Phys. Rev. Lett. 63(14), 219–223 (1989)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Kumar, B.V., Mahalanobis, A., Juday, R.D.: Correlation Pattern Recognition, p. 402. Cambridge University Press (2006)Google Scholar
  37. 37.
    Lin, C.-T., Lee, C.S.: Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice Hall (1998)Google Scholar
  38. 38.
    Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Addison-Wesley (2008)Google Scholar
  39. 39.
    Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review 3(3), 127–149 (2009)CrossRefGoogle Scholar
  40. 40.
    Maass, W., Natschlaeger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)MATHCrossRefGoogle Scholar
  41. 41.
    Maimon, O., Rokach, L. (eds.): Data Mining and Knowledge Discovery Handbook, 2nd edn. Springer (2010)Google Scholar
  42. 42.
    Mandelbrot, B.: The Fractal Geometry of Nature, p. 468. W.H. Freeman (1983)Google Scholar
  43. 43.
    Mira, J.M.: Symbols versus connections: 50 years of artificial intelligence. Neurocompuing 71, 671–680 (2008)CrossRefGoogle Scholar
  44. 44.
    Mosekilde, E., Maistrenko, Y., Postnov, D.: Chaotic synchronization. World Scientific Series on Nonlinear Science, Series A vol. 42, 440 (2002)MathSciNetGoogle Scholar
  45. 45.
    Oliveira, F.: Limitations of learning in automata-based systems. European Journal of Operational Research 203, 684–691 (2010)MathSciNetMATHCrossRefGoogle Scholar
  46. 46.
    Pedrycz, W., Weber, R.: Special issue on soft computing for dynamic data mining. Applied Soft Computing (8), 1281–1282 (2008)Google Scholar
  47. 47.
    Peitgen, H., Jürgens, H., Dietmar, S.: Chaos and Fractals. New Frontiers of Science, 2nd edn., vol. XIII(864), p. 125 illus (2004) ISBN: 978-0-387-20229-7Google Scholar
  48. 48.
    Pikovsky, A., Maistrenko, Y.: Synchronization: Theory and Application. NATO Science Series II: Mathematics, Physics and Chemistry, p. 268. Springer (2008) ISBN- 9781402014178Google Scholar
  49. 49.
    Potapov, A.V., Ali, M.K.: Nonlinear dynamics and chaos in information processing neural networks. Differential Equations and Dynamical Systems 9(3-4), 259–319 (2001)MathSciNetMATHGoogle Scholar
  50. 50.
    Preparata, F.R., Shamos, M.I.: Computational Geometry. An Introduction. Monographs in Computer Science, p. 398. Springer (1993)Google Scholar
  51. 51.
    Prigogine, I.: Order Out of Chaos. Shambala (1984)Google Scholar
  52. 52.
    Rothemund, P.W.K.: A DNA and restriction enzyme implementation of Turing machines. DNA Based Computers 6, 75–120 (1996)MathSciNetGoogle Scholar
  53. 53.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall (2002)Google Scholar
  54. 54.
    Schweitzer, F.: Self-Organization of Complex Structures: From Individual to Collective Dynamics. CRC Press (1997)Google Scholar
  55. 55.
    Simon, H.A.: Artificial intelligence: an empirical science. Artificial Intelligence 77, 95–127 (1995)CrossRefGoogle Scholar
  56. 56.
    Teuscher, C.: Turing’s Connectionism An Investigation of Neural Network Architectures (2002)Google Scholar
  57. 57.
    Saunders, P.T. (ed.): Turing, A. M. Collected Works of A. M. TUring: Morphogenesis. North-Holland (1992)Google Scholar
  58. 58.
    Britton, J.L. (ed.): Turing, A. M. Collected Works of A. M. Turing: Pure Mathematics. North-Holland (1992)Google Scholar
  59. 59.
    Ince, D.C. (ed.): Turing, A. M. Collected Works of A. M. TUring: Mechanical Intelligence. North-Holland (1992)Google Scholar
  60. 60.
    Gandy, R., Yates, C. (eds.): Turing A. M. Collected Works of A. M. Turing-Mathematical Logic. Elsevier (2001)Google Scholar
  61. 61.
    Ultsch, A.: Clustering with SOM: U*C. In: Proc. Workshop on Self-Organizing Maps, Paris, France, pp. 75–82 (2005)Google Scholar
  62. 62.
    Velazquez, J.: Brain, behaviour and mathematics: Are we using the right approaches? Physica D 212, 161–182 (2005)MathSciNetCrossRefGoogle Scholar
  63. 63.
    Webster, C.S.: Alan Turing’s unorganized machines and artificial neural networks: his remarkable early work and future possibilities. Evolutionary Intelligence, 1–9 (July 22, 2011)Google Scholar
  64. 64.
    Wolfram, S.: A New Kind of Science. Wolfram Media (2002)Google Scholar
  65. 65.
    Zak, M.: Quantum-inspired resonance for associative memory. Chaos, Solitons and Fractals 41, 2306–2312 (2009)MathSciNetMATHCrossRefGoogle Scholar
  66. 66.
    Zbilut, J.P., Giuliani, A.: Biological uncertainty Theory Bioscience 127 (2008)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Computer ScienceSt. Petersburg State Polytechnical UniversitySt. PetersburgRussia
  2. 2.Graduate School of ManagementSt. Petersburg State UniversitySt. PetersburgRussia

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