Skip to main content

Physical and Formal Aspects of Computation: Exploiting Physics for Computation and Exploiting Computation for Physical Purposes

  • Chapter
  • First Online:
Book cover Advances in Unconventional Computing

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 22))

Abstract

Achieving greater speeds and densities in the post-Moore’s Law era will require computation to be more like the physical processes by which it is realized. Therefore we explore the essence of computation, that is, what distinguishes computational processes from other physical processes. We consider such issues as the topology of information processing, programmability, and universality. We summarize general characteristics of analog computation, quantum computation, and field computation, in which data is spatially continuous. Computation is conventionally used for information processing, but since the computation governs physical processes, it can also be used as a way of moving matter and energy on a microscopic scale. This provides an approach to programmable matter and programmed assembly of physical structures. We discuss artificial morphogenesis, which uses the formal structure of embryological development to coordinate the behavior of a large number of agents to assemble complex hierarchical structures. We explain that this close correspondence between computational and physical processes is characteristic of embodied computation, in which computation directly exploits physical processes for computation, or for which the physical consequences of computation are the purpose of the computation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The \(\infty \)-product metric on the Cartesian product of two spaces \((X,\delta _1)\), \((Y, \delta _2)\) is defined \(\delta _\infty [(x,y), (x',y')] = \max [\delta _1(x,x'), \delta _2(y,y')]\).

References

  1. Adamatzky, A.: Physarum Machines: Computers from Slime Mould. World Scientific Series on Nonlinear Science Series A, vol. 74. World Scientific, Singapore (2010)

    Google Scholar 

  2. Adamatzky, A., De Lacy Costello, B., Asai, T.: Reaction-Diffusion Computers. Elsevier, Amsterdam (2005)

    Google Scholar 

  3. Ambs, P.: Optical computing: A 60-year adventure. Adv. Opt. Technol. 2010, Article ID 372,652 (2010). doi:10.1155/2010/372652

  4. Bennett, C.H.: Notes on Landauer’s principle, reversible computation, and Maxwell’s demon. Stud. Hist. Philos. Mod. Phys. 34, 501–510 (2003)

    Article  MATH  Google Scholar 

  5. Blum, L., Cucker, F., Shub, M., Smale, S.: Complexity and Real Computation. Springer, Berlin (1998)

    Book  MATH  Google Scholar 

  6. Bourgine, P., Lesne, A. (eds.): Morphogenesis: Origins of Patterns and Shapes. Springer, Berlin (2011)

    Google Scholar 

  7. Bray, D.: Wetware: A Computer in Every Living Cell. Yale University Press, New Haven (2009)

    Google Scholar 

  8. Brockett, R.: Dynamical systems that sort lists, diagonalize matrices and solve linear programming problems. In: Proceedings of the 27th IEEE Conference Decision and Control, pp. 799–803. Austin, TX (1988)

    Google Scholar 

  9. Brooks, R.: Intelligence without representation. Artif. Intell. 47, 139–159 (1991)

    Article  Google Scholar 

  10. Clark, A.: Being There: Putting Brain, Body, and World Together Again. MIT Press, Cambridge (1997)

    Google Scholar 

  11. Clark, A., Chalmers, D.J.: The extended mind. Analysis 58(7), 10–23 (1998)

    Google Scholar 

  12. Connor, R.J., Holleman, J., MacLennan, B.J., Smith, J.M.: Simulation of analog solution of Boolean satisfiability. Technical Report UT-EECS-15-735, University of Tennessee, Department of Electrical Engineering and Computer Science, Knoxville (2015)

    Google Scholar 

  13. Das, A., Chakrabarti, B.K.: Colloquium : quantum annealing and analog quantum computation. Rev. Mod. Phys. 80, 1061–1081 (2008). http://link.aps.org/doi/10.1103/RevModPhys.80.1061

  14. Doursat, R.: Organically grown architectures: creating decentralized, autonomous systems by embryomorphic engineering. In: Würtz, R.P. (ed.) Organic Computing, pp. 167–200. Springer, Heidelberg (2008)

    Google Scholar 

  15. Doursat, R., Sayama, H., Michel, O. (eds.): Morphogenetic Engineering: Toward Programmable Complex Systems. Springer, Heidelberg (2012)

    Google Scholar 

  16. Dreyfus, H.L.: What Computers Still Can’t Do. MIT Press, New York (1992)

    Google Scholar 

  17. Ercsey-Ravasz, M., Toroczkai, Z.: Optimization hardness as transient chaos in an analog approach to constraint satisfaction. Nature Phys. 7, 966–970 (2011)

    Article  Google Scholar 

  18. Giavitto, J., Spicher, A.: Computer morphogenesis. In: Bourgine, P., Lesne, A. (eds.) Morphogenesis: Origins of Patterns and Shapes, pp. 315–340. Springer, Berlin (2011)

    Google Scholar 

  19. Goldstein, S.C., Campbell, J.D., Mowry, T.C.: Programmable matter. Computer 38(6), 99–101 (2005)

    Article  Google Scholar 

  20. Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Pearson Education, New York (2008)

    Google Scholar 

  21. Iida, F., Pfeifer, R., Steels, L., Kuniyoshi, Y.: Embodied Artificial Intelligence. Springer, Berlin (2004)

    Book  Google Scholar 

  22. Johnson, M., Rohrer, T.: We are live creatures: Embodiment, American pragmatism, and the cognitive organism. In: Zlatev, J., Ziemke, T., Frank, R., Dirven, R. (eds.) Body, Language, and Mind, vol. 1, pp. 17–54. Mouton de Gruyter, Berlin (2007)

    Google Scholar 

  23. Kitano, H.: Morphogenesis for evolvable systems. In: Sanchez, E., Tomassini, M. (eds.) Towards Evolvable Hardware: The Evolutionary Engineering Approach, pp. 99–117. Springer, Berlin (1996)

    Google Scholar 

  24. Landauer, R.: The physical nature of information. Phys. Lett. A 217, 188 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  25. Lipshitz, L., Rubel, L.A.: A differentially algebraic replacment theorem. Proc. Am. Math. Soc. 99(2), 367–372 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  26. Lloyd, S., Braunstein, S.L.: Quantum computation over continuous variables. Phys. Rev. Lett. 82, 1784–1787 (1999). http://link.aps.org/doi/10.1103/PhysRevLett.82.1784

  27. MacLennan, B.J.: Technology-independent design of neurocomputers: the universal field computer. In: Caudill, M., Butler, C. (eds.) In: Proceedings of the IEEE First International Conference on Neural Networks, vol. 3, pp. 39–49. IEEE Press (1987)

    Google Scholar 

  28. MacLennan, B.J.: Field computation in the brain. In: Pribram, K. (ed.) Rethinking Neural Networks: Quantum Fields and Biological Data, pp. 199–232. Lawrence Erlbaum, Hillsdale (1993). http://web.eecs.utk.edu/~mclennan

  29. MacLennan, B.J.: Continuous formal systems: A unifying model in language and cognition. In: Proceedings of the IEEE Workshop on Architectures for Semiotic Modeling and Situation Analysis in Large Complex Systems, pp. 161–172. Monterey, CA (1995). http://web.eecs.utk.edu/+mclennan and http://cogprints.org/541

  30. MacLennan, B.J.: Field computation in natural and artificial intelligence. Inf. Sci. 119, 73–89 (1999). http://web.eecs.utk.edu/~mclennan

  31. MacLennan, B.J.: Natural computation and non-Turing models of computation. Theor. Comput. Sci. 317, 115–145 (2004)

    Google Scholar 

  32. MacLennan, B.J.: Analog computation (chap. 1, entry 19). In: Meyers, R. et al. (ed.) Encyclopedia of Complexity and System Science, pp. 271—294. Springer, Heidelberg (2009). doi:10.1007/978-0-387-30440-3_19. Reprinted in Computational Complexity: Theory, Techniques, and Applications, ed. by Meyers, R.A. et al., Springer, 2012, pp. 161–184

  33. MacLennan, B.J.: Field computation in natural and artificial intelligence (chap. 6, entry 199). In: Meyers, R. et al. (ed.) Encyclopedia of Complexity and System Science, pp. 3334–3360. Springer, Heidelberg (2009). doi:10.1007/978-0-387-30440-3_199

  34. MacLennan, B.J.: Preliminary development of a formalism for embodied computation and morphogenesis. Technical Report UT-CS-09-644, Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN (2009)

    Google Scholar 

  35. MacLennan, B.J.: Super-Turing or non-Turing? Extending the concept of computation. Int. J. Unconv. Comput. 5(3–4), 369–387 (2009)

    Google Scholar 

  36. MacLennan, B.J.: Models and mechanisms for artificial morphogenesis. In: Peper, F., Umeo, H., Matsui, N., Isokawa, T. (eds.) Natural Computing, Springer series, Proceedings in Information and Communications Technology (PICT) vol. 2, pp. 23–33. Springer, Tokyo (2010)

    Google Scholar 

  37. MacLennan, B.J.: Morphogenesis as a model for nano communication. Nano Commun. Netw. 1(3), 199–208 (2010). doi:10.1016/j.nancom.2010.09.007

    Article  Google Scholar 

  38. MacLennan, B.J.: The U-machine: a model of generalized computation. Int. J. Unconv. Comput. 6(3–4), 265–283 (2010)

    Google Scholar 

  39. MacLennan, B.J.: Artificial morphogenesis as an example of embodied computation. Int. J. Unconv. Comput. 7(1–2), 3–23 (2011)

    Google Scholar 

  40. MacLennan, B.J.: Bodies – both informed and transformed: Embodied computation and information processing. In: Dodig-Crnkovic, G., Burgin, M. (eds.) Information and Computation. World Scientific Series in Information Studies, vol. 2, pp. 225–253. World Scientific, Singapore (2011)

    Google Scholar 

  41. MacLennan, B.J.: Embodied computation: applying the physics of computation to artificial morphogenesis. Parallel Process. Lett. 22(3) (2012)

    Google Scholar 

  42. MacLennan, B.J.: Molecular coordination of hierarchical self-assembly. Nano Commun. Netw. 3(2), 116–128 (2012)

    Article  Google Scholar 

  43. MacLennan, B.J.: Coordinating massive robot swarms. Int. J. Robot. Appl. Technol. 2(2), 1–19 (2014). doi:10.4018/IJRAT.2014070101

    Google Scholar 

  44. MacLennan, B.J.: The promise of analog computation. Int. J. Gen.Syst. 43(7), 682–696 (2014). doi:10.1080/03081079.2014.920997

    Article  MathSciNet  MATH  Google Scholar 

  45. MacLennan, B.J.: The morphogenetic path to programmable matter. Proc. IEEE 103(7), 1226–1232 (2015)

    Article  Google Scholar 

  46. Meinhardt, H.: Models of Biological Pattern Formation. Academic Press, London (1982)

    Google Scholar 

  47. Menary, R. (ed.): The Extended Mind. MIT Press, Cambridge (2010)

    Google Scholar 

  48. Molnár, B., Ercsey-Ravasz, M.: Asymmetric continuous-time neural networks without local traps for solving constraint satisfaction problems. PLoS ONE 8(9), e73,400 (2013). doi:10.1371/journal.pone.0073400

  49. Murata, S., Kurokawa, H.: Self-reconfigurable robots: shape-changing cellular robots can exceed conventional robot flexibility. IEEE Robot. Autom. Mag. pp. 71–78 (2007)

    Google Scholar 

  50. Nagpal, R., Kondacs, A., Chang, C.: Programming methodology for biologically-inspired self-assembling systems. In: AAAI Spring Symposium on Computational Synthesis: From Basic Building Blocks to High Level Functionality (2003). http://www.eecs.harvard.edu/ssr/papers/aaaiSS03-nagpal.pdf

  51. Nemytskii, V.V., Stepanov, V.V.: Qualitative Differential Equations, Reprint of 1960 Princeton Univ, Press edn. Dover, New York, NY (1989)

    Google Scholar 

  52. Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information, 10th anniversary edn. Cambridge University Press, Cambridge (2010)

    Google Scholar 

  53. Pfeifer, R., Bongard, J.: How the Body Shapes the Way We Think – A New View of Intelligence. MIT Press, Cambridge (2007)

    Google Scholar 

  54. Pfeifer, R., Lungarella, M., Iida, F.: Self-organization, embodiment, and biologically inspired robotics. Science 318, 1088–93 (2007)

    Article  Google Scholar 

  55. Pfeifer, R., Scheier, C.: Understanding Intelligence. MIT Press, Cambridge (1999)

    Google Scholar 

  56. Popa, C.R.: Synthesis of Computational Structures for Analog Signal Processing. Springer, New York (2011)

    MATH  Google Scholar 

  57. Pour-El, M.: Abstract computability and its relation to the general purpose analog computer (some connections between logic, differential equations and analog computers). Trans. Am. Math. Soc. 199, 1–29 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  58. Rothemund, P., Papadakis, N., Winfree, E.: Algorithmic self-assembly of DNA Sierpinski triangles. PLoS Biol. 2(12), 2041–2053 (2004)

    Article  Google Scholar 

  59. Rothemund, P., Winfree, E.: The program-size complexity of self-assembled squares. In: Symposium on Theory of Computing (STOC), pp. 459–68. Association for Computing Machinery, New York (2000)

    Google Scholar 

  60. Rupp, K., Selberherr, S.: The economic limit to Moore’s law. IEEE Trans. Semicond. Manuf. 24(1), 1–4 (2011). doi:10.1109/TSM.2010.2089811

    Article  Google Scholar 

  61. Santoro, G.E., Tosatti, E.: Optimization using quantum mechanics: quantum annealing through adiabatic evolution. J. Phys. A: Math. Gen. 39(36), R393 (2006). http://stacks.iop.org/0305-4470/39/i=36/a=R01

  62. Shannon, C.E.: Mathematical theory of the differential analyzer. J. Math. Phys. Mass. Institute Technol. 20, 337–354 (1941)

    MathSciNet  MATH  Google Scholar 

  63. Shannon, C.E.: Mathematical theory of the differential analyzer. In: Sloane, N.J.A., Wyner, A.D. (eds.) Claude Elwood Shannon: Collected Papers, pp. 496–513. IEEE Press, New York (1993)

    Google Scholar 

  64. Spicher, A., Michel, O., Giavitto, J.: Algorithmic self-assembly by accretion and by carving in MGS. In: Proceedings of the 7th International Conference on Artificial Evolution (EA ‘05), no. 3871 in Lecture Notes in Computer Science, pp. 189–200. Springer, Berlin (2005)

    Google Scholar 

  65. Turing, A.: The chemical basis of morphogenesis. Philos. Trans. R. Soc. B 237, 37–72 (1952)

    Article  Google Scholar 

  66. van Gelder, T.: Dynamics and cognition (chap. 16). In: Haugeland, J. (ed.) Mind Design II: Philosophy, Psychology and Artificial Intelligence, revised & enlarged edn., pp. 421–450. MIT Press, Cambridge (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bruce J. MacLennan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

MacLennan, B.J. (2017). Physical and Formal Aspects of Computation: Exploiting Physics for Computation and Exploiting Computation for Physical Purposes. In: Adamatzky, A. (eds) Advances in Unconventional Computing. Emergence, Complexity and Computation, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-33924-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-33924-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33923-8

  • Online ISBN: 978-3-319-33924-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics