A CA-Based Self-organizing Environment: A Configurable Adaptive Illumination Facility

The term Ambient Intelligence refers to electronic environments that are sensitive and responsive to the presence of people; in this paper an example of ambient intelligence whose goal is to enhance the experience of pedestrians moving inside the related physical environment will be presented. In the described approach the environment itself is endowed with a set of sensors (to perceive humans or other physical entities such as dogs, bicycles, etc.), interacting with a set of actuators that choose their actions in an attempt improve the overall experience of these users; in particular, the introduced system realizes an adaptive illumination facility. The model for the interaction and action of sensors and actuators is a dissipative multilayered cellular automata, 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 the results of simulations of its application in a concrete case study.

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

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

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

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

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

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

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

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

[9]  Nicholas R. Jennings,et al.  On agent-based software engineering , 2000, Artif. Intell..

[10]  Chris Hanson,et al.  Amorphous computing , 2000, Commun. ACM.

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

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

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

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

[15]  Franco Zambonelli,et al.  Software Engineering for Large-Scale Multi-Agent Systems , 2003, Lecture Notes in Computer Science.

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

[17]  Paul L. Rosin.  Training Cellular Automata for Image Processing , 2006, IEEE Trans. Image Process..

[18]  William Butera.  Text Display and Graphics Control on a Paintable Computer , 2007, First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007).

[19]  Emil C. Lupu,et al.  Towards Supporting Interactions between Self-Managed Cells , 2007, First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007).

[20]  Radhika Nagpal,et al.  Automated global-to-local programming in 1-D spatial multi-agent systems , 2008, AAMAS.

[21]  E. Goles,et al.  Neural and Automata Networks: Dynamical Behavior and Applications , 2011 .