Enabling Creativity through Innovation Challenges: The Case of Interactive Lightning

  • Stefania Bandini
  • Andrea Bonomi
  • Giuseppe Vizzari
  • Vito Acconci


This paper discusses a case in which an idea and a creative design for a reactive environment characterized by an adaptive lightning expressed by an artist was transformed into a prototype supporting the customization of the lightning effect. A specific configuration interface was realized to support the user in expressing and envisioning his creativity: by altering some specific parameters of the model implemented in the system, he/she can effectively change the behaviour of the adaptive lightning and immediately visualize the implications of his choice on parameters’ values. This experience has been further developed towards the realization of a product based on the same model and approach: a configurable modular adaptive lightning system. The paper presents the starting scenario and its main characteristics, then the model supporting adaptive lightning is introduced. The model configuration and envisioning interface is described and the recent developments towards a product based on the model and configuration system conclude the paper.


Cellular Automaton Cellular Automaton Transition Rule Cellular Automaton Model Distribute Control 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Mogensen, K.Æ. (ed.): Creative Man. The Copenhagen Institute for Futures Studies, (Last accessed May 2010)
  2. 2.
    Potts, J.: Art & innovation: An evolutionary economic view of the creative industries. UNESCO Observatory, Faculty of Architecture, Building and Planning, The University of Melbourne Refereed E-Journal, Multi-Disciplinary Research in the Arts 1(1) (2007)Google Scholar
  3. 3.
    von Neumann, J.: Theory of Self-Reproducing Automata. University of Illinois Press, Urbana (1966)Google Scholar
  4. 4.
    Weimar, J.R.: Simulation with Cellular Automata. Logos Verlag, Berlin (1997)Google Scholar
  5. 5.
    Rosin, P.L.: Training cellular automata for image processing. IEEE Transactions on Image Processing 15(7), 2076–2087 (2006)CrossRefGoogle Scholar
  6. 6.
    Behring, C., Bracho, M., Castro, M., Moreno, J.A.: An algorithm for robot path planning with cellular automata. In: Bandini, S., Worsch, T. (eds.) ACRI, pp. 11–19. Springer, Heidelberg (2000)Google Scholar
  7. 7.
    Hillis, W.D.: The Connection Machine. MIT Press, Cambridge (1985)Google Scholar
  8. 8.
    Margolus, N., Toffoli, T.: Cellular Automata Machines. In: A New Environment for Modelling. MIT Press, Cambridge (1987)Google Scholar
  9. 9.
    Paolo, E.A.D.: Searching for rhythms in asynchronous random boolean networks. In: Bedau, M. (ed.) Alife VII: Proceedings of the Seventh International Conference, pp. 73–80. MIT Press, Cambridge (2000)Google Scholar
  10. 10.
    Thomas, R., Organization, E.M.B.: Kinetic logic: A Boolean approach to the analysis of complex regulatory systems. In: Thomas, R. (ed.) Proceedings of the EMBO Course Formal Analysis of Genetic Regulation, held in Brussels, September 6-16. Springer, Berlin (1979)Google Scholar
  11. 11.
    Nehaniv, C.L.: Evolution in asynchronous cellular automata. In: ICAL 2003: Proceedings of the eighth international conference on Artificial life, pp. 65–73. MIT Press, Cambridge (2003)Google Scholar
  12. 12.
    Cornforth, D., Green, D.G., Newth, D.: Ordered asynchronous processes in multi-agent systems. Physica D: Nonlinear Phenomena 204(1-2), 70–82 (2005)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Zambonelli, F., Mamei, M., Roli, A.: What can cellular automata tell us about the behavior of large multi-agent systems? In: Garcia, A.F., de Lucena, C.J.P., Zambonelli, F., Omicini, A., Castro, J. (eds.) SELMAS. LNCS, vol. 2603, pp. 216–231. Springer, Heidelberg (2002)Google Scholar
  14. 14.
    Buvel, R.L., Ingerson, T.E.: Structure in asynchronous cellular automata. Physica D 1, 59–68 (1984)MathSciNetGoogle Scholar
  15. 15.
    Lumer, E.D., Nicolis, G.: Synchronous versus asynchronous dynamics in spatially distributed systems. Phys. D 71(4), 440–452 (1994)zbMATHCrossRefGoogle Scholar
  16. 16.
    Alonso-Sanz, R.: The beehive cellular automaton with memory. Journal of Cellular Automata 1(3), 195–211 (2006)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Bandini, S., Bonomi, A., Vizzari, G., Acconci, V.: Simulation of alternative self-organization models for an adaptive environment. In: Proceedings of the Second Multi-Agent Logics, Languages, and Organisations Federated Workshops, Turin, Italy, CEUR Workshop Proceedings 494 (2009)Google Scholar

Copyright information

© Springer Netherlands 2011

Authors and Affiliations

  • Stefania Bandini
    • 1
  • Andrea Bonomi
    • 1
  • Giuseppe Vizzari
    • 1
  • Vito Acconci
    • 2
  1. 1.University of Milano-BicoccaItaly
  2. 2.Acconci StudioUSA

Personalised recommendations