Skip to main content
SpringerLink
Log in

Multi-agent Slime Mould Computing: Mechanisms, Applications and Advances

  • Chapter
  • First Online:
Advances in Physarum Machines

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 21))

  • 1334 Accesses

Abstract

The giant single-celled slime mould Physarum polycephalum has inspired developments in bio-inspired computing and unconventional computing substrates since the start of this century. This is primarily due to its simple component parts and the distributed nature of the ‘computation’ which it approximates during its growth, foraging and adaptation to a changing environment. Slime mould functions as a living embodied computational material which can be influenced by external stimuli. The goal of exploiting this material behaviour for unconventional computation led to the development of a simple multi-agent approach to the approximation of slime mould behaviour. The basis of the model is a simple dynamical pattern formation mechanism which exhibits self-organised formation and subsequent adaptation of collective transport networks. The system exhibits emergent properties such as relaxation and minimisation and it can be considered as a virtual material, influenced by the external application of spatial concentration gradients. In this chapter we give an overview of this multi-agent approach to unconventional computing. We describe its computational mechanisms and different generic application domains, together with concrete example applications of material computation. We examine the potential exploitation of the approach for computational geometry, path planning, combinatorial optimisation, data smoothing and statistical approximation applications.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
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

References

  1. Adamatzky, A.: Physarum machines: encapsulating reaction-diffusion to compute spanning tree. Naturwissenschaften 94(12), 975–980 (2007)

    Article  Google Scholar 

  2. Adamatzky, A.: Developing proximity graphs by Physarum polycephalum: does the plasmodium follow the toussaint hierarchy. Parallel Process. Lett. 19, 105–127 (2008)

    Article  MathSciNet  Google Scholar 

  3. Adamatzky, A.: Hot ICE computer. Phys. Lett. A 374(2), 264–271 (2009)

    Article  Google Scholar 

  4. Adamatzky, A.: If BZ medium did spanning trees these would be the same trees as Physarum built. Phys. Lett. A 373(10), 952–956 (2009)

    Article  MATH  Google Scholar 

  5. Adamatzky, A.: Physarum Machines: Computers from Slime Mould, vol. 74. World Scientific Publishing Company, Inc., (2010)

    Google Scholar 

  6. Adamatzky, A.: Slime mould computes planar shapes. Int. J. Bio-Inspired Comput. 4(3), 149–154 (2012)

    Article  Google Scholar 

  7. Adamatzky, A., de Lacy Costello B., Shirakawa, T.: Universal computation with limited resources: Belousov-zhabotinsky and Physarum computers. Int. J. Bifurcat. Chaos 18(8), 2373–2389 (2008)

    Google Scholar 

  8. Adamatzky, A., Jones, J.: Programmable reconfiguration of Physarum machines. Nat. Comput. 9(1), 219–237 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  9. Aono, M., Hirata, Y., Hara, M., Aihara, K.: Amoeba-based chaotic neurocomputing: combinatorial optimization by coupled biological oscillators. New Gener. Comput. 27(2), 129–157 (2009)

    Article  MATH  Google Scholar 

  10. Asai, T., De-Lacy Costello, B., Adamatzky, A.: Silicon implementation of a chemical reaction-diffusion processor for computation of voronoi diagram. Int. J. Bifurcat. Chaos 15(10), 3307–3320 (2005)

    Google Scholar 

  11. Baumgarten, W., Jones, J., Hauser, M.J.B. Network coarsening dynamics in a plasmodial slime mould: modelling and experiments. Acta Physica Polonica B 46(6) (2015). In-press

    Google Scholar 

  12. Boschetti, F., Gray, R.: A turing test for emergence. Advances in applied self-organizing systems, pp. 349–364 (2008)

    Google Scholar 

  13. Cohen, L.D.: On active contour models and balloons. CVGIP: Image Underst. 53(2), 211–218 (1991)

    Article  MATH  Google Scholar 

  14. De Berg, M., Cheong, O., Van Kreveld, M.: Computational geometry: algorithms and applications. Springer, New York (2008)

    Google Scholar 

  15. De Boor, C.: A practical guide to splines, vol. 27. Springer, New York (1978)

    Google Scholar 

  16. de Lacy Costello, B., Ratcliffe, N., Adamatzky, A., Zanin, A.L., Liehr, A.W., Purwins, H.G.: The formation of Voronoi diagrams in chemical and physical systems: experimental findings and theoretical models. Int. J. Bifurcat. Chaos Appl. Sci. Eng. 14(7), 2187–2210 (2004)

    Google Scholar 

  17. Dorigo, M., Bonabeau, E., Theraulaz, G.: Ant algorithms and stigmergy. Future Gener. Comput. Syst. 16(8), 851–871 (2000)

    Article  Google Scholar 

  18. Dorigo, M., Stutzle, T.: Ant colony optimization (2004)

    Google Scholar 

  19. Duckham, M., Kulik, L., Worboys, M., Galton, A.: Efficient generation of simple polygons for characterizing the shape of a set of points in the plane. Pattern Recogn. 41(10), 3224–3236 (2008)

    Article  MATH  Google Scholar 

  20. Durbin, R., Willshaw, D.: An analogue approach to the travelling salesman problem using an elastic net method. Nature 326(6114), 689–691 (1987)

    Article  Google Scholar 

  21. Edelsbrunner, H., Kirkpatrick, D., Seidel, R.: On the shape of a set of points in the plane. Inf. Theor, IEEE Trans. 29(4), 551–559 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  22. Eilers, P.H.C., Marx, B.D.: Flexible smoothing with b-splines and penalties. Stat. Sci. 89–102 (1996)

    Google Scholar 

  23. Foretník, J.: Architektura, geometrie a vỳpočetní technika. Ph.D. thesis (2010)

    Google Scholar 

  24. Fortune, S.: A sweepline algorithm for voronoi diagrams. Algorithmica 2(1), 153–174 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  25. Salomaa, A., Paun, G., Rozenberg, G.: DNA Computing: New Computing Paradigms. Texts in Theoretical Computer Science. Springer, New York (1998)

    Google Scholar 

  26. Galton, A., Duckham, M.: What is the region occupied by a set of points? In: Raubal, M., Miller, H.J., Frank, A.U., Goodchild, M.F. (eds.) GIScience 2006. LNCS, vol. 4197, pp. 81–98. Springer, Heidelberg (2006)

    Google Scholar 

  27. Gunji, Y.-P., Shirakawa, T., Niizato, T., Haruna, T.: Minimal model of a cell connecting amoebic motion and adaptive transport networks. J. Theor. Biol. 253(4), 659–667 (2008)

    Article  Google Scholar 

  28. Gunji, Y.-P., Shirakawa, T., Niizato, T., Yamachiyo, M., Tani, I.: An adaptive and robust biological network based on the vacant-particle transportation model. J. Theor. Biol. 272(1), 187–200 (2011)

    Article  Google Scholar 

  29. Hasegawa, M.: Verification and rectification of the physical analogy of simulated annealing for the solution of the traveling salesman problem. Phys. Rev. E 83(3), 036708 (2011)

    Article  MathSciNet  Google Scholar 

  30. Heimann, T., Meinzer, H.-P.: Statistical shape models for 3d medical image segmentation: a review. Med. Image Anal. 13(4), 543–563 (2009)

    Article  Google Scholar 

  31. Hickey, D.S., Noriega, L.A.: Relationship between structure and information processing in Physarum polycephalum. Int. J. Model. Ident. Control 4(4), 348–356 (2008)

    Article  Google Scholar 

  32. Hopfield, J.J., Tank, D.W.: Computing with neural circuits: a model. Science 233(4764), 625 (1986)

    Article  MATH  Google Scholar 

  33. Hou, H., Andrews, H.: Cubic splines for image interpolation and digital filtering. Acoust., Speech Signal Process., IEEE Trans. 26(6), 508–517 (1978)

    Article  MATH  Google Scholar 

  34. Ishiguro, A., Shimizu, M., Kawakatsu, T.: Don’t try to control everything!: an emergent morphology control of a modular robot. In: Proceedings of 2004 IEEE/RSJ international conference on intelligent robots and systems, pp. 981–985. Sendai, Japan, Sept 28–Oct 2 2004

    Google Scholar 

  35. Jaromczyk, J.W., Toussaint, G.T.: Relative neighborhood graphs and their relatives. Proc. IEEE 80(9), 1502–1517 (1992)

    Article  Google Scholar 

  36. Jarvis, R.A.: On the identification of the convex hull of a finite set of points in the plane. Inf. Process. Lett. 2(1), 18–21 (1973)

    Article  MATH  Google Scholar 

  37. Jones, J., Mayne, R., Adamatzky, A.: Representation of shape mediated by environmental stimuli in physarum polycephalum and a multi-agent model. Int. J. Parallel, Emergent Distrib. Syst. 0(0), 1–19 (2015)

    Google Scholar 

  38. Jones, J.: Characteristics of pattern formation and evolution in approximations of Physarum transport networks. Artif. Life 16(2), 127–153 (2010)

    Article  Google Scholar 

  39. Jones, J.: The emergence and dynamical evolution of complex transport networks from simple low-level behaviours. Int. J. Unconventional Comput. 6(2), 125–144 (2010)

    Google Scholar 

  40. Jones, J.: Influences on the formation and evolution of physarum polycephalum inspired emergent transport networks. Nat. Comput. 10(4), 1345–1369 (2011)

    Article  MathSciNet  Google Scholar 

  41. Jones, J.: Towards programmable smart materials: Dynamical reconfiguration of emergent transport networks. Int. Journal of Unconventional Comput. 7(6), 423–447 (2011)

    Google Scholar 

  42. Jones, J.: From pattern formation to material computation: multi-agent modelling of physarum polycephalum, vol. 15. Springer, New York (2015)

    Google Scholar 

  43. Jones, J.: Mechanisms inducing parallel computation in a model of physarum polycephalum transport networks. Parallel Process. Lett. 25(01), 1540004 (2015)

    Article  MathSciNet  Google Scholar 

  44. Jones, J.: A morphological adaptation approach to path planning inspired by slime mould. Int. J. Gen Syst 44(3), 279–291 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  45. Jones, J., Adamatzky, A.: Emergence of self-organized amoeboid movement in a multi-agent approximation of Physarum polycephalum. Bioinspiration Biomimetics 7(1), 016009 (2012)

    Article  Google Scholar 

  46. Jones, J., Adamatzky, A.: Slime mould inspired generalised voronoi diagrams with repulsive fields. In-Press, Int. J. Bifurcat. Chaos (2013)

    Google Scholar 

  47. Jones, J., Adamatzky, A.: Approximation of statistical analysis and estimation by morphological adaptation in a model of slime mould. Int. J. Unconventional Comput., In Press (2014)

    Google Scholar 

  48. Jones, J., Adamatzky, A.: Computation of the travelling salesman problem by a shrinking blob. Nat. Comput. 13(1), 1–16 (2014)

    Article  MathSciNet  Google Scholar 

  49. Jones, J., Adamatzky, A.: Material approximation of data smoothing and spline curves inspired by slime mould. Bioinspiration Biomimetics 9(3), 036016 (2014)

    Article  Google Scholar 

  50. Jump, J.A.: Studies on sclerotization in physarum polycephalum. Am. J. Botany, pp. 561–567 (1954)

    Google Scholar 

  51. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vision 1(4), 321–331 (1988)

    Article  MATH  Google Scholar 

  52. Koza, J., Poli, R.: Genetic Programming. Search Methodologies, pp. 127–164 (2005)

    Google Scholar 

  53. Larranaga, P., Kuijpers, C.M.H., Murga, R.H., Inza, I., Dizdarevic, S.: Genetic algorithms for the travelling salesman problem: a review of representations and operators. Artif. Intell. Rev. 13(2), 129–170 (1999)

    Article  Google Scholar 

  54. Lihoreau, M., Chittka, L., Raine, N.E.: Travel optimization by foraging bumblebees through readjustments of traplines after discovery of new feeding locations. Am. Nat. 176(6), 744–757 (2010)

    Article  Google Scholar 

  55. Matsumoto, K., Ueda, T., Kobatake, Y.: Reversal of thermotaxis with oscillatory stimulation in the plasmodium of Physarum polycephalum. J. Theor. Biol. 131, 175–182 (1988)

    Article  Google Scholar 

  56. Mitchell, M.: An introduction to genetic algorithms. MIT Press, Cambridge, MA, USA (1996)

    MATH  Google Scholar 

  57. Murray, J.D.: On pattern formation mechanisms for lepidopteran wing patterns and mammalian coat markings. Philos. Trans. Royal Soc. Lond. B, Biol. Sci., 295(1078), 473–496 (1981)

    Google Scholar 

  58. Nakagaki, T., Kobayashi, R., Nishiura, Y., Ueda, T.: Obtaining multiple separate food sources: behavioural intelligence in the Physarum plasmodium. R. Soc. Proc.: Biol. Sci., 271(1554), 2305–2310 (2004)

    Google Scholar 

  59. Oster, G.F., Odell, G.M.: Mechanics of cytogels I: oscillations in Physarum. Cell Motil. 4(6), 469–503 (1984)

    Article  Google Scholar 

  60. Pershin, Y.V., La Fontaine, S., Di Ventra, M.: Memristive model of amoeba learning. Phys. Rev. E 80(2), 021926 (2009)

    Article  Google Scholar 

  61. Privman, V., Arugula, M.A., Halámek, J., Pita, M., Katz, E.: Network analysis of biochemical logic for noise reduction and stability: A system of three coupled enzymatic and gates. J. Phys. Chem. B 113(15), 5301–5310 (2009)

    Article  Google Scholar 

  62. Radszuweit, M., Engel, H., Bär, M.: A model for oscillations and pattern formation in protoplasmic droplets of Physarum polycephalum. Eur. Phys. Journal-Special Top. 191(1), 159–172 (2010)

    Article  Google Scholar 

  63. Reinsch, C.H.: Smoothing by spline functions. Numerische mathematik 10(3), 177–183 (1967)

    Article  MathSciNet  Google Scholar 

  64. Reyes, D.R., Ghanem, M.M., Whitesides, G.M., Manz, A.: Glow discharge in microfluidic chips for visible analog computing. Lab Chip 2(2), 113–116 (2002)

    Article  Google Scholar 

  65. Ronald, E.M.A., Sipper, M., Capcarrère, M.S.: Design, observation, surprise! a test of emergence. Artif. Life 5(3), 225–239 (1999)

    Article  Google Scholar 

  66. Saigusa, T., Tero, A., Nakagaki, T., Kuramoto, Y.: Amoebae anticipate periodic events. Phys. Rev. Lett. 100(1), 18101 (2008)

    Article  Google Scholar 

  67. Sawa, K., Balaž, I., Shirakawa, T.: Cell motility viewed as softness. Int. J. Artif. Life Res. (IJALR) 3(1), 1–9 (2012)

    Article  Google Scholar 

  68. Sellares, J.A., Toussaint, G.: On the role of kinesthetic thinking in computational geometry. Int. J. Math. Edu. Sci. Technol. 34(2), 219–237 (2003)

    Article  Google Scholar 

  69. Sherratt, J.A., Lewis, J.: Stress-induced alignment of actin filaments and the mechanics of cytogel. Bull. Math. Biol. 55(3), 637–654 (1993)

    Article  MATH  Google Scholar 

  70. Shirakawa, T., Adamatzky, A., Gunji, Y.-P., Miyake, Y.: On simultaneous construction of voronoi diagram and delaunay triangulation by Physarum polycephalum. Int. J. Bifurcat. Chaos 19(9), 3109–3117 (2009)

    Article  Google Scholar 

  71. Shirakawa, T., Gunji, Y.-P.: Computation of Voronoi diagram and collision-free path using the Plasmodium of Physarum polycephalum. Int. J. Unconventional Comput. 6(2), 79–88 (2010)

    Google Scholar 

  72. Stepney, S.: The neglected pillar of material computation. Physica D 237(9), 1157–1164 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  73. Takagi, S., Ueda, T.: Emergence and transitions of dynamic patterns of thickness oscillation of the plasmodium of the true slime mold Physarum polycephalum. Physica D 237, 420–427 (2008)

    Article  Google Scholar 

  74. Takamatsu, A., Takaba, E., Takizawa, G.: Environment-dependent morphology in plasmodium of true slime mold Physarum polycephalum and a network growth model. J. Theor. Biol. 256(1), 29–44 (2009)

    Article  MathSciNet  Google Scholar 

  75. Takamatsu, A., Takahashi, K., Nagao, M., Tsuchiya, Y.: Frequency coupling model for dynamics of responces to stimuli in plasmodium of Physarum polycephalum. J. Phys. Soc. Jpn. 66, 1638–1646 (1997)

    Article  Google Scholar 

  76. Teplov, V.A., Romanovsky, Y.M., Latushkin, O.A.: A continuum model of contraction waves and protoplasm streaming in strands of Physarum plasmodium. Biosystems 24(4), 269–289 (1991)

    Article  Google Scholar 

  77. Tero, A., Kobayashi, R., Nakagaki, T.: A coupled-oscillator model with a conservation law for the rhythmic amoeboid movements of plasmodial slime molds. Physica D 205(1), 125–135 (2005)

    Article  MATH  Google Scholar 

  78. Tero, A., Kobayashi, R., Nakagaki, T.: Physarum solver: a biologically inspired method of road-network navigation. Phys. A 363(1), 115–119 (2006)

    Article  Google Scholar 

  79. Tero, A., Nakagaki, T., Toyabe, K., Yumiki, K., Kobayashi, R.: A method inspired by Physarum for solving the steiner problem. Int. J. Unconventional Comput. 6, 109–123 (2010)

    Google Scholar 

  80. Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D.P., Fricker, M.D., Yumiki, K., Kobayashi, R., Nakagaki, T.: Rules for biologically inspired adaptive network design. Science 327(5964), 439–442 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  81. Tero, A., Yumiki, K., Kobayashi, R., Saigusa, T., Nakagaki, T.: Flow-network adaptation in Physarum amoebae. Theory Biosci. 127(2), 89–94 (2008)

    Article  Google Scholar 

  82. Tolmachiev, D., Adamatzky, A.: Chemical processor for computation of voronoi diagram. Adv. Mater. Opt. Electron. 6(4), 191–196 (1996)

    Article  Google Scholar 

  83. Toussaint, G.T.: The relative neighbourhood graph of a finite planar set. Pattern Recogn. 12(4), 261–268 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  84. Tsuda, S., Jones, J.: The emergence of synchronization behavior in Physarum polycephalum and its particle approximation. Biosystems 103, 331–341 (2010)

    Article  Google Scholar 

  85. Tsuda, S., Jones, J., Adamatzky, A.: Towards Physarum engines. Appl. Bion. Biomech. 9(3), 221–240 (2012)

    Article  Google Scholar 

  86. Turk, G.: Generating textures on arbitrary surfaces using reaction-diffusion. Comput. Graph. 25(4), 289–298 (1991)

    Article  Google Scholar 

  87. Zanin, A.L., Liehr, A.W., Moskalenko, A.S., Purwins, H.G.: Voronoi diagrams in barrier gas discharge. Appl. Phys. Lett. 81, 3338 (2002)

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the EU research project “Physarum Chip: Growing Computers from Slime Mould” (FP7 ICT Ref 316366).

Author information

Authors and Affiliations

  1. Centre for Unconventional Computing, University of the West of England, Bristol, BS16 1QY, UK

    Jeff Jones

Authors
  1. Jeff Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeff Jones .

Editor information

Editors and Affiliations

  1. Unconventional Computing Centre, University of the West of England, Bristol, United Kingdom

    Andrew Adamatzky

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Jones, J. (2016). Multi-agent Slime Mould Computing: Mechanisms, Applications and Advances. In: Adamatzky, A. (eds) Advances in Physarum Machines. Emergence, Complexity and Computation, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-319-26662-6_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26662-6_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26661-9

  • Online ISBN: 978-3-319-26662-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation