A Mapping Function to Use Cellular Automata for Solving MAS Problems

  • Andreas Goebels
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


Cellular automata are a very powerful and well researched area in computer science. We use approaches from the cellular automata research to solve optimization problems in the multi agent system research area. For this purpose, we require a transformation from agents located in an Euclidean space into an abstract cell assignment for cellular automata. In this paper, a mapping function is presented and evaluated with a reverse function. This function can be calculated by each agent individually based only on local information. Additionally, we examine the performance of the function in inexact and non-deterministic environments.


Cellular Automaton Mapping Function Cellular Automaton Swarm Intelligence Reasonable Interval 
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  1. 1.
    Goebels, A., Weimer, A., Priesterjahn, S.: Using cellular automata with evolutionary learned rules to solve the online partitioning problem. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), Edinburgh, pp. 837–843 (2005)Google Scholar
  2. 2.
    Panait, L., Luke, S.: Cooperative multi-agent learning: The state of the art. In: Technical Report GMU-CS-TR-2003-1, George Mason University, USA (2003)Google Scholar
  3. 3.
    Gacs, P., Kurdyumov, G.L., Levin, L.: One-dimensional uniform arrays that wash out finite islands. In: Problemy Peredachi Informatsii, vol. 12, pp. 92–98 (1978)Google Scholar
  4. 4.
    Goebels, A., Büning, H.K., Priesterjahn, S., Weimer, A.: Towards online partitioning of agent sets based on local information. In: Proceedings of the International Conference on Parallel and Distributed Computing and Networks (PDCN), Innsbruck, Austria (2005)Google Scholar
  5. 5.
    Mitchell, M., Hraber, P.T., Crutchfield, J.P.: Revisiting the edge of chaos: Evolving cellular automata to perform computations (1993)Google Scholar
  6. 6.
    Boris, J.: A vectorized ’near neighbors’ algorithm of order n using a monotonic logical grid. J. Comput. Phys. 66, 1–20 (1986)MATHCrossRefGoogle Scholar
  7. 7.
    Andre, D., Bennett III, F., Koza, J.: Discovery by genetic programming of a cellular automata rule that is better than any known rule for the majority classification problem. In: Proceedings of the First Annual Conference on Genetic Programming. MIT Press, Cambridge (1996)Google Scholar
  8. 8.
    Juille, H., Pollack, J.: Coevolving the ideal trainer: Application to the discovery of cellular automata rules. In: Proceedings of the Third Annual Genetic Programming Conference (GP 1998) (1998)Google Scholar
  9. 9.
    Pagie, L., Mitchell, M.: A comparison of evolutionary and coevolutionary search. In: Belew, R.K., Juillé, H. (eds.) Coevolution: Turning Adaptive Algorithms upon Themselves, USA, pp. 20–25 (2001)Google Scholar
  10. 10.
    Werfel, J., Mitchell, M., Crutchfield, J.: Resource sharing and coevolution in evolving cellular automata. IEEE Transactions on Evolutionary Computation 4(4), 388 (2000)CrossRefGoogle Scholar
  11. 11.
    Kirchner, M.: Genetisch gesteuertes Lernen von Regeln für eindimensionale zelluläre Automaten. Bachelor thesis, Universität Paderborn (2005)Google Scholar
  12. 12.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence - From natural to artificial Systems. Oxford University Press, Oxford (1999)MATHGoogle Scholar
  13. 13.
    Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)Google Scholar
  14. 14.
    Wang, Y., Li, X.Y.: Distributed spanner with bounded degree for wireless ad hoc networks. In: Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing (2002)Google Scholar
  15. 15.
    Eppstein, D.: Spanning trees and spanners. Technical Report Technical Report 96-16, University of California, Dept. Information and Computer Science (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andreas Goebels
    • 1
  1. 1.International Graduate School of Dynamic Intelligent Systems, Knowledge Based SystemsUniversity of PaderbornGermany

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