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Ant-Based Computing

  • Loizos Michael
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3630)

Abstract

We propose a biologically and physically plausible model for ants and pheromones, and show this model to be sufficiently powerful to simulate the computation of arbitrary logic circuits. We thus establish that coherent deterministic and centralized computation can emerge from the collective behavior of simple distributed markovian processes as those followed by ants.

Keywords

Choice Point Output Gain Circuit Level Pheromone Concentration Primitive Component 
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.
    Abelson, H., Allen, D., Coore, D., Hanson, C., Homsy, G., Knight, T.F., Nagpal, R., Rauch, E., Sussman, G.J., Weiss, R.: Amorphous Computing. Communications of the Association for Computing Machinery 43(5) (2000)Google Scholar
  2. 2.
    Bonabeau, E., Theraulaz, G., Deneubourg, J.-L.: Fixed Response Thresholds and the Regulation of Division of Labour in Insect Societies. Bulletin of Mathematical Biology 60, 753–807 (1998)zbMATHCrossRefGoogle Scholar
  3. 3.
    Deneubourg, J.-L., Aron, S., Goss, S., Pasteels, J.: The Self-Organising Exploratory Pattern of the Argentine Ant. Journal of Insect Behavior 3, 159–168 (1990)CrossRefGoogle Scholar
  4. 4.
    Deneubourg, J.-L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chrétien, L.: The Dynamics of Collective Sorting: Robot-Like Ants and Ant-Like Robots. In: Proceedings of the First International Conference on Simulation of Adaptive Behavior: From Animals to Animats (1991)Google Scholar
  5. 5.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, U.S.A. (2004)zbMATHCrossRefGoogle Scholar
  6. 6.
    Knight, T.F., Sussman, G.J.: Cellular Gate Technology. In: Proceedings of the First International Conference on Unconventional Models of Computation (1998)Google Scholar
  7. 7.
    Lumer, E., Faieta, B.: Diversity and Adaption in Populations of Clustering Ants. In: Proceedings of the Third International Conference on Simulation of Adaptive Behaviour: From Animals to Animats (1994)Google Scholar
  8. 8.
    Vestad, T., Marr, D.W.M., Munakata, T.: Flow Resistance for Microfluidic Logic Operations. Applied Physics Letters 84, 5074–5075 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Loizos Michael
    • 1
  1. 1.Division of Engineering and Applied SciencesHarvard UniversityCambridgeU.S.A

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