Imitation Programming Unorganised Machines

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

Abstract

In 1948 Alan Turing presented a general representation scheme by which to achieve artificial intelligence – his unorganised machines. Further, at the same time as also suggesting that natural evolution may provide inspiration for search, he noted that mechanisms inspired by the cultural aspects of learning may prove useful. This chapter presents results from an investigation into using Turing’s dynamical network representation designed by a new imitation-based, i.e., cultural, approach. Moreover, the original synchronous and an asynchronous form of unorganised machines are considered, along with their implementation in memristive hardware.

Keywords

Particle Swarm Optimization Genetic Program Cellular Automaton Cellular Automaton Discrete Dynamical System 
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.

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References

  1. 1.
    Afifi, A., Ayatollahi, A., Raissi, F.: STDP implementation using memristive nanodevice in CMOS-Nano neuromorphic networks. IEICE Electronics Express 6(3), 148–153 (2009)CrossRefGoogle Scholar
  2. 2.
    Aldana, M., Cluzel, P.: A natural class of robust networks. PNAS 100(15), 8710–8714 (2003)CrossRefGoogle Scholar
  3. 3.
    Andre, D., Koza, J.R., Bennett, F.H., Keane, M.: Genetic Programming III. MIT (1999)Google Scholar
  4. 4.
    Angeline, P.: Evolutionary Optimization vs Particle Swarm Optimization. In: Porto, V.W., et al. (eds.) Proceedings of Evolutionary Programming 7, pp. 601–610. Springer (1998)Google Scholar
  5. 5.
    Angeline, P., Saunders, G., Pollock, J.: An Evolutionary Algorithm that Constructs Recurrent Neural Networks. IEEE Transactions on Neural Networks 5, 54–65 (1994)CrossRefGoogle Scholar
  6. 6.
    Atkeson, C., Schaal, S.: Robot learning from demonstration. In: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 12–20. Morgan Kaufmann (1997)Google Scholar
  7. 7.
    Billard, A., Dautenhahn, K.: Experiments in Learning by Imitation - Grounding and Use of Communication in Robotic Agents. Adaptive Behavior 7(3/4), 415–438 (1999)CrossRefGoogle Scholar
  8. 8.
    Borghetti, J., Snider, G., Kuekes, P., Yang, J., Stewart, D., Williams, R.S.: ‘Memristive’ switches enable ‘stateful’ logic operations via material implication. Nature 464, 873–876 (2010)CrossRefGoogle Scholar
  9. 9.
    Brave, S.: Evolving Deterministic Finite Automata using Cellular Encoding. In: Koza, J.R., et al. (eds.) Procs of the First Ann. Conf. on Genetic Programming, pp. 39–44. MIT Press (1996)Google Scholar
  10. 10.
    Bull, L.: Using Genetical and Cultural Search to Design Unorganised Machines. Evolutionary Intelligence 5(1), 23–34 (2012)CrossRefGoogle Scholar
  11. 11.
    Chua, L.O.: Memristor - the missing circuit element. IEEE Trans. Circuit Theory 18, 507–519 (1971)CrossRefGoogle Scholar
  12. 12.
    Copeland, J.: The Essential Turing. Oxford (2004)Google Scholar
  13. 13.
    Dawkins, R.: The Selfish Gene. Oxford (1976)Google Scholar
  14. 14.
    Di, J., Lala, P.: Cellular Array-based Delay Insensitive Asynchronous Circuits Design and Test for Nanocomputing Systems. Journal of Electronic Testing 23, 175–192 (2007)CrossRefGoogle Scholar
  15. 15.
    Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through A Simulation of Evolution. In: Maxfield, M., et al. (eds.) Biophysics and Cybernetic Systems: Proceedings of the 2nd Cybernetic Sciences Symposium, pp. 131–155. Spartan Books (1965)Google Scholar
  16. 16.
    Gershenson, C.: Classification of Random Boolean Networks. In: Standish, R.K., Bedau, M., Abbass, H. (eds.) Artificial Life VIII, pp. 1–8. MIT Press (2002)Google Scholar
  17. 17.
    Gorman, B., Humphreys, M.: Towards Integrated Imitation of Strategic Planning and Motion Modeling in Interactive Computer Games. Computers in Entertainment 4(4) (2006)Google Scholar
  18. 18.
    Gruau, F., Whitley, D.: Adding Learning to the Cellular Development Process. Evolutionary Computation 1(3), 213–233 (1993)CrossRefGoogle Scholar
  19. 19.
    Hassdijk, E., Vogt, P., Eiben, A.: Social Learning in Population-based Adaptive Systems. In: Procs of the 2008 IEEE Congress on Evolutionary Computation. IEEE Press (2008)Google Scholar
  20. 20.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. Univ. of Mich. Press (1975)Google Scholar
  21. 21.
    Howard, D., Gale, E., Bull, L., de Lacy Costello, B., Adamatzky, A.: Evolving Spiking Networks with Variable Memristor Synapses. In: GECCO-2011: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1275–1282. ACM Press (2011)Google Scholar
  22. 22.
    Hutchins, E., Hazelhurst, B.: Learning in the Cultural Process. In: Langton, C.G., et al. (eds.) Artificial Life II, pp. 689–706. Addison Wesley (1990)Google Scholar
  23. 23.
    Kauffman, S.A.: The Origins of Order. Oxford (1993)Google Scholar
  24. 24.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Press (1995)Google Scholar
  25. 25.
    Koza, J.R.: Genetic Programming. MIT Press (1992)Google Scholar
  26. 26.
    Luke, S., Spector, L.: Evolving Graphs and Networks with Edge Encoding: Preliminary Report. In: Koza, J.R. (ed.) Late Breaking Papers at the Genetic Programming 1996 Conference, Stanford University, pp. 117–124 (1996)Google Scholar
  27. 27.
    McCulloch, W.S., Pitts, W.: A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943)MathSciNetMATHCrossRefGoogle Scholar
  28. 28.
    Miller, J.: An Empirical Study of the Efficiency of Learning Boolean Functions using a Cartesian Genetic Programming Approach. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference – GECCO 1999, pp. 1135–1142. Morgan Kaufmann (1999)Google Scholar
  29. 29.
    Mitchell, M., Hraber, P., Crutchfield, J.: Revisiting the Edge of Chaos: Evolving Cellular Automata to Perform Computations. Complex Systems 7, 83–130 (1993)Google Scholar
  30. 30.
    Nakamura, K.: Asynchronous Cellular Automata and their Computational Ability. Systems, Computers, Controls 5(5), 58–66 (1974)MathSciNetGoogle Scholar
  31. 31.
    Packard, N.: Adaptation Toward the Edge of Chaos. In: Kelso, J., Mandell, A., Shlesinger, M. (eds.) Dynamic Patterns in Complex Systems, pp. 293–301. World Scientific (1988)Google Scholar
  32. 32.
    Poli, R.: Parallel Distributed Genetic Programming. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimisation, pp. 403–431. McGraw-Hill (1999)Google Scholar
  33. 33.
    Preen, R., Bull, L.: Discrete Dynamical Genetic Programming in XCS. In: GECCO 2009: Proceedings of the Genetic and Evolutionary Computation Conference. ACM Press (2009)Google Scholar
  34. 34.
    Price, B., Boutilier, C.: Implicit Imitation in Multiagent Reinforcement learning. In: Procs of Sixteenth Intl Conference on Machine Learning, pp. 325–334. Morgan Kaufmann (1999)Google Scholar
  35. 35.
    Reynolds, R.: An Introduction to Cultural Algorithms. In: Sebald, Fogel, D. (eds.) Procs of 3rd Ann. Conf. on Evolutionary Programming, pp. 131–139. World Scientific (1994)Google Scholar
  36. 36.
    Schmidt, M., Lipson, H.: Comparison of Tree and Graph Encodings as Function of Problem Complexity. In: Proceedings of the Genetic and Evolutionary Computation Conference – GECCO 2007, pp. 1674–1679. ACM Press (2007)Google Scholar
  37. 37.
    Sipper, M.: Evolution of Parallel Cellular Machines. Springer (1997)Google Scholar
  38. 38.
    Sipper, M., Tomassini, M., Capcarrere, S.: Evolving Asynchronous and Scalable Non-uniform Cellular Automata. In: Proceedings of the Third International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 66–70. Springer (1997)Google Scholar
  39. 39.
    Storn, R., Price, K.: Differential Evolution - a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization 11, 341–359 (1997)MathSciNetMATHCrossRefGoogle Scholar
  40. 40.
    Teller, A., Veloso, M.: Neural Programming and an Internal Reinforcement Policy. In: Koza, J.R. (ed.) Late Breaking Papers at the Genetic Programming 1996 Conference, Stanford University, pp. 186–192 (1996)Google Scholar
  41. 41.
    Teuscher, C.: Turing’s Connectionism. Springer (2002)Google Scholar
  42. 42.
    Thompson, A., Harvey, I., Husbands, P.: Unconstrained Evolution and Hard Consequences. In: Sanchez, E., Tomassini, M. (eds.) Proceedings of First International Conference on Evolvable Hardware Towards Evolvable Hardware. Springer (1996)Google Scholar
  43. 43.
    Turing, A.: Intelligent Machinery. In: Evans, C.R., Robertson, A. (eds.) Key Papers: Cybernetics, pp. 91–102. Butterworths (1968)Google Scholar
  44. 44.
    Upegui, A., Sanchez, E.: Evolving Hardware with Self-reconfigurable connectivity in Xilinx FPGAs. In: Proceedings of the first NASA/ESA Conference on Adaptive Hardware and Systems, pp. 153–162. IEEE Press (2006)Google Scholar
  45. 45.
    von Neumann, J.: The Theory of Self-Reproducing Automata. University of Illinois (1966)Google Scholar
  46. 46.
    Werner, T., Akella, V.: Asynchronous Processor Survey. Comput. 30(11), 67–76 (1997)CrossRefGoogle Scholar
  47. 47.
    Whitehead, A.N., Russell, B.: Principia Mathematica, vol. I, p. 7. Cambridge University Press (1910)Google Scholar
  48. 48.
    Widrow, B.: An adaptive ADALINE neuron using chemical Memistors. Stanford Electronics Laboratories Technical Report 1553-2 (1960)Google Scholar
  49. 49.
    Widrow, B., Hoff, M.E.: Adaptive Switching Circuits. In: 1960 IRE WESCON Convention Record, IRE pp. 96–104 (1960)Google Scholar
  50. 50.
    Wyatt, D., Bull, L.: A Memetic Learning Classifier System for Describing Continuous-Valued Problem Spaces. In: Krasnagor, N., Hart, W., Smith, J. (eds.) Recent Advances in Memetic Algorithms, pp. 355–396. Springer (2004)Google Scholar
  51. 51.
    Yang, J.J., et al.: Memristive Switching Mechanism for Metal/Oxide/Metal Nanodevices. Nature Nanotechnology 3, 429–433 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer Science & Creative TechnologiesUniversity of the West of EnglandBristolU.K

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