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Abstract

Nature is not afraid of complexity. Her solutions exploit the unpredictable and messy nature of reality. But our technology seems to be very different. Instead of exploiting its environment it is more frequently damaged by that environment. In this article I describe how we can learn from natural systems and create new technologies that exploit natural principles. I describe our investigations into the technologies of the future – devices that can adapt, be fault tolerant, and even assemble themselves. Examples of a self-repairing robot and physical self-assembling systems are shown, and I describe my systemic computer concept which aims to be the first parallel fault tolerant computer that is based on general biological systems. Through examples such as these, I argue that while we may never be able to predict exactly what a natural system may do, that does not prevent such systems from being extremely useful for us – after all, we are unpredictable natural systems ourselves.

Keywords

Short Path Natural System Systemic Computation Robot Controller Robot Snake 
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.
    Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, London (2004)zbMATHGoogle Scholar
  2. 2.
    Kari, L., Rozenberg, G.: The many facets of natural computing. Communications of the ACM 51, 72–83 (2008)CrossRefGoogle Scholar
  3. 3.
    Bentley, P.J.: Digitized, Oxford, UK (2012)Google Scholar
  4. 4.
    von Neumann, J.: First Draft of a Report on the EDVAC. Moore School of Electrical Engineering, University of Pennsylvania. Developed under contract W-670-ORD-4926 between the United States Army Ordinance Department and the University of Pennsylvania (1945)Google Scholar
  5. 5.
    Obituary on Turing in The Times (1954)Google Scholar
  6. 6.
    Shannon, C.: Presentation of a Maze-Solving Machine. Group Interchange. In: Macy Jr., J. (ed.) Transactions of the Eighth Conference on Cybernetics Foundation, March 15-16, pp. 173–180 (1951)Google Scholar
  7. 7.
    Shannon, C.: Programming a Computer for Playing Chess. Philosophical Magazine, Ser.7 41(314) (March 1950)Google Scholar
  8. 8.
    von Neumann, J.: The Computer and the Brain: 2 edn. (Mrs. Hepsa Ely Silliman Memorial Lectures) (2000)Google Scholar
  9. 9.
    von Neumann, J., Burks, A.W.: Theory of Self-Reproducing Automata. University of Illinois Press, Urbana (1966)Google Scholar
  10. 10.
    Haroun Mahdavi, S., Bentley, P.J.: Innately adaptive robotics through embodied evolution. Journal of Adaptive Robotics (2004)Google Scholar
  11. 11.
    Haroun Mahdavi, S., Bentley, P.J.: Innately adaptive robotics through embodied evolution. In: Proc. of Robosphere 2004, the 2nd Workshop on Self-Sustaining Robotic Systems, November 9-10. NASA Ames Research Center (2004)Google Scholar
  12. 12.
    Mahdavi, S.H., Bentley, P.J.: An Evolutionary Approach to Damage Recovery of Robot Motion With Muscles. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 248–255. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Shannon, C.E.: A Mathematical Theory of Communication. Bell System Technical Journal 27, 379–423, 623–656 (1948)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Shannon, C.: Communication Theory of Secrecy Systems. Bell System Technical Journal 28(4), 656–715 (1948)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Bhalla, N., Bentley, P.J., Vise, C., Jacob, C.: Programming and Evolving Self-assembling Systems in Three Dimensions. To appear in the Special issue on Engineering Emergence, in the Journal of Natural Computing (2011)Google Scholar
  16. 16.
    Bhalla, N., Bentley, P.J.: Programming Self-assembling Systems Via Physically Encoded Information. In: Doursat, Sayama, Michel (eds.) Morphogenetic Engineering. Springer, Heidelberg (2011)Google Scholar
  17. 17.
    Bhalla, N., Bentley, P.J., Jacob, C.: Mapping Virtual Self-assembly Rules to Physical Systems. In: Proc. of the 2007 Conference on Unconventional Computing, Bristol, July 12-14 (2007)Google Scholar
  18. 18.
    Bentley, P.J.: Systemic computation: A model of interacting systems with natural characteristics. IJPEDS 22, 103–121 (2007)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Bentley, P.J.: Methods for Improving Simulations of Biological Systems: Systemic Computation and Fractal Proteins. Special Issue on Synthetic Biology, J R Soc. Interface 2009 6, 451–466 (2009), doi:10.1098/rsif.2008.0505.focusGoogle Scholar
  20. 20.
    Tammet, D.: Embracing the Wide Sky: A Tour Across the Horizons of the Mind. Hodder & Stoughton (2009)Google Scholar
  21. 21.
    Le Martelot, E., Bentley, P.J., Lotto, R.B.: A Systemic Computation Platform for the Modelling and Analysis of Processes with Natural Characteristics. In: GECCO 2007, pp. 2809–2816. ACM Press (2007)Google Scholar
  22. 22.
    Rouhipour, M., Bentley, P.J., Shayani, H.: Systemic Computation Using Graphics Processors. In: Tempesti, G., Tyrrell, A.M., Miller, J.F. (eds.) ICES 2010. LNCS, vol. 6274, pp. 121–132. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  23. 23.
    Sakellariou, C., Bentley, P.J.: Introducing the FPGA-Based Hardware Architecture of Systemic Computation (HAoS). In: Kotásek, Z., et al. (eds.) MEMICS 2011. LNCS, vol. 7119, pp. 179–190. Springer, Heidelberg (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Peter J. Bentley
    • 1
  1. 1.Department of Computer ScienceUniversity College of LondonLondonUK

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