Emergent Spatial Patterns in Vegetable Population Dynamics: Towards Pattern Detection and Interpretation

  • Stefania Bandini
  • Sara Manzoni
  • Stefano Redaelli
  • Leonardo Vanneschi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


In this paper we present an ongoing research that aims at providing an interpretation and detection method for spatial patterns supporting ecosystem management in the study of forest systems according to a distributed modeling and simulation approach. To this aim an innovative analysis method inspired by the Chinese Go game is under design. The originality of the approach concerns the detection within system configurations of known patterns whose interpretations are well–known by expert Go players.


Cellular Automaton Cellular Automaton Independent Component Analysis Pattern Detection Forest System 
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.


  1. 1.
    Bandini, S., Pavesi, G.: Simulation of vegetable populations dynamics based on cellular automata. In: Bandini, S., Chopard, B., Tomassini, M. (eds.) ACRI 2002. LNCS, vol. 2493. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Green, D.G.: Modelling plants in landscape. In: Michalewicz, T. (ed.) Plants to Ecosystem – Harek. CSIRO, Lollingwood Ans (1997)Google Scholar
  3. 3.
    Wolfram, S.: Cellular automata as models of complexity. Nature 311, 419–424 (1984)CrossRefGoogle Scholar
  4. 4.
    Reysset, P.: Le Go: aux sources de l’avenir, Chiron (1994)Google Scholar
  5. 5.
    Soletti, G.: Note di Go. FIGG (Federazione Italiana Giuoco Go). Avaiable for download at,
  6. 6.
    Bandini, S., Manzoni, S., Redaelli, S.: Toward the Interpretation of Emergent Spatial Patterns through GO Game: The Case of Forest Population Dynamics. In: Proceedings of Simulation and Formal Analysis of Complex Systems, WOA 2005 (2005)Google Scholar
  7. 7.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. Wiley, New York (2001)MATHGoogle Scholar
  8. 8.
    Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)MATHGoogle Scholar
  9. 9.
    Babuska, R.: Fuzzy clustering algorithms with applications to rule extraction. In: Szczepaniak, P.S., Lisboa, P.J.G. (eds.) Fuzzy Systems in Medicine, pp. 139–173. Springer, Heidelberg (2000)Google Scholar
  10. 10.
    Pal, S.K., Wang, P.P.: Genetic Algorithms and Pattern Recognition. CRC Press, Boca Raton (1996)Google Scholar
  11. 11.
    Bunke, H., Kandel, A.: Hybrid methods in pattern recognition. World Scientific Series in Machine Perception and Artificial Intelligence vol. 47 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stefania Bandini
    • 1
  • Sara Manzoni
    • 1
  • Stefano Redaelli
    • 1
  • Leonardo Vanneschi
    • 1
  1. 1.Dept. of Informatics, Systems, and CommunicationUniversity of Milan–BicoccaMilanItaly

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