A Cellular Automata-Based Modular Lighting System

The term Ambient Intelligence refers to environments enhanced by the presence of electronic devices that are sensitive and responsive to the presence of people. The scenario described in the paper envisages an environment endowed with a set of sensors (to perceive humans or other physical entities), interacting with a set of actuators (lights) that adjust their state of illumination in an attempt to improve the overall experience of these users. The model for the interaction and action of sensors and actuators is an asynchronous Cellular Automata (CA) supporting a self-organization of the system as a response to the presence and movements of people inside it. The paper will introduce the model as well as its implementation in a specific hardware component supporting the realization of modular adaptive lighting systems.

[1]  Stefania Bandini,et al.  Parallel simulation of reaction-diffusion phenomena in percolation processes : A model based on cellular automata , 2001, Future Gener. Comput. Syst..

[2]  Grégoire Nicolis,et al.  Synchronous versus asynchronous dynamics in spatially distributed systems , 1994 .

[3]  S. Levin Lectu re Notes in Biomathematics , 1983 .

[4]  Franco Zambonelli,et al.  What Can Cellular Automata Tell Us about the Behavior of Large Multi-agent Systems? , 2002, SELMAS.

[5]  Tommaso Toffoli,et al.  Cellular Automata Machines , 1987, Complex Syst..

[6]  W. Daniel Hillis,et al.  The connection machine , 1985 .

[7]  Nigel Shadbolt,et al.  Brain power , 2003, IEEE Intelligent Systems.

[8]  T. E. Ingerson,et al.  Structure in asynchronous cellular automata , 1984 .

[9]  Nazim Fatès,et al.  An Experimental Study of Robustness to Asynchronism for Elementary Cellular Automata , 2004, Complex Syst..

[10]  Tommaso Toffoli,et al.  Cellular automata machines - a new environment for modeling , 1987, MIT Press series in scientific computation.

[11]  Hussein A. Abbass,et al.  Proceedings of the eighth international conference on Artificial life , 2002 .

[12]  Stefania Bandini,et al.  Multilayered Cellular Automata , 1999, Theor. Comput. Sci..

[13]  Stefania Bandini,et al.  An Asynchronous Cellular Automata-Based Adaptive Illumination Facility , 2009, AI*IA.

[14]  John von Neumann,et al.  Theory Of Self Reproducing Automata , 1967 .

[15]  David G. Green,et al.  Ordered asynchronous processes in multi-agent systems , 2005 .

[16]  Chrystopher L. Nehaniv Evolution in asynchronous cellular automata , 2002 .

[17]  John S. McCaskill,et al.  Searching for Rhythms in Asynchronous Random Boolean Networks , 2000 .

[18]  Jörg R. Weimar,et al.  Simulation with Cellular Automata , 2003 .

[19]  H. Gutowitz Cellular automata: theory and experiment : proceedings of a workshop , 1991 .

[20]  René Thomas,et al.  Kinetic logic : a Boolean approach to the analysis of complex regulatory systems : proceedings of the EMBO course "Formal analysis of genetic regulation," held in Brussels, September 6-16, 1977 , 1979 .

[21]  Nigel Shadbolt,et al.  From the Editor in Chief: Grandly Challenged , 2003, IEEE Intell. Syst..

[22]  Paul L. Rosin Training cellular automata for image processing , 2005, IEEE Transactions on Image Processing.

[23]  Stefania Bandini,et al.  Implementing Cellular Automata Based Models on Parallel Architectures: The CAPP Project , 1999, PaCT.

[24]  Stefania Bandini,et al.  A CA-Based Self-organizing Environment: A Configurable Adaptive Illumination Facility , 2009, PaCT.

[25]  M. Castro,et al.  An Algorithm for Robot Path Planning with Cellular Automata , 2000, ACRI.

[26]  Ramón Alonso-Sanz The Beehive Cellular Automaton with Memory , 2006, J. Cell. Autom..

[27]  Victor E. Malyshkin,et al.  Parallel computing technologies , 2011, The Journal of Supercomputing.