Emergence and control of macro-spatial structures in perturbed cellular automata, and implications for pervasive computing systems

Predicting the behavior of complex decentralized pervasive computing systems before their deployment in a dynamic environment, as well as being able to influence and control their behavior in a decentralized way, will be of fundamental importance in the near future. In this context, this paper describes the general behavior observed in a large set of asynchronous cellular automata when external perturbations influence the internal activities of cellular automata cells. In particular, we observed that stable macrolevel spatial structures emerge from local interactions among cells, a behavior that does not emerge when cellular automata are not perturbed. Similar sorts of macrolevel behaviors are likely to emerge in the context of pervasive computing systems and need to be studied, controlled, and possibly fruitfully exploited. On this basis, the paper also reports the results of a set of experiments, showing how it is possible to control, in a decentralized way, the behavior of perturbed cellular automata, to make any desired patterns emerge.

[1]  Radhika Nagpal,et al.  Experimental Results for and Theoretical Analysis of a Self-Organizing Global Coordinate System for Ad Hoc Sensor Networks , 2004, Telecommun. Syst..

[2]  Franco Zambonelli,et al.  Programming pervasive and mobile computing applications with the TOTA middleware , 2004, Second IEEE Annual Conference on Pervasive Computing and Communications, 2004. Proceedings of the.

[3]  I. Prigogine,et al.  Exploring Complexity: An Introduction , 1989 .

[4]  Franco Zambonelli,et al.  Location-dependent services for mobile users , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[5]  Radhika Nagpal,et al.  Self-Reconfiguration Using Directed Growth , 2004, DARS.

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

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

[8]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[9]  Nicholas R. Jennings,et al.  Agent-based control systems: Why are they suited to engineering complex systems? , 2003 .

[10]  Leigh Tesfatsion,et al.  Agent-Based Computational Economics: Growing Economies From the Bottom Up , 2002, Artificial Life.

[11]  Moshe Tennenholtz,et al.  Artificial Social Systems , 1992, Lecture Notes in Computer Science.

[12]  Gabor Karsai,et al.  Smart Dust: communicating with a cubic-millimeter computer , 2001 .

[13]  Franco Zambonelli,et al.  Spray computers: frontiers of self-organization for pervasive computing , 2004, 13th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises.

[14]  Nicholas R. Jennings,et al.  Agent-based control systems , 2003 .

[15]  H. Van Dyke Parunak,et al.  ERIM's Approach to Fine-Grained Agents , 2001 .

[16]  M. Sipper,et al.  The Emergence of Cellular Computing , 1999, Computer.

[17]  Liviu Iftode,et al.  Cooperative computing for distributed embedded systems , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[18]  David L. Tennenhouse,et al.  Proactive computing , 2000, Commun. ACM.

[19]  Stuart A. Kauffman,et al.  ORIGINS OF ORDER , 2019, Origins of Order.

[20]  Les Gasser,et al.  Object-based concurrent programming and distributed artificial intelligence , 1992 .

[21]  Albert-László Barabási,et al.  Internet: Diameter of the World-Wide Web , 1999, Nature.

[22]  F. Peper,et al.  Computation by Asynchronously Updating Cellular Automata , 2004 .

[23]  M Segev,et al.  Modulation instability and pattern formation in spatially incoherent light beams. , 2000, Science.

[24]  Yaneer Bar-Yam,et al.  Dynamics Of Complex Systems , 2019 .

[25]  Tim D. Barfoot,et al.  Multiagent Coordination by Stochastic Cellular Automata , 2001, IJCAI.

[26]  H. Van Dyke Parunak,et al.  "Go to the ant": Engineering principles from natural multi-agent systems , 1997, Ann. Oper. Res..

[27]  Franco Zambonelli,et al.  Developing multiagent systems: The Gaia methodology , 2003, TSEM.

[28]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[29]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[30]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[31]  Radhika Nagpal,et al.  Programmable self-assembly using biologically-inspired multiagent control , 2002, AAMAS '02.

[32]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[33]  Yi-Ming Wei,et al.  The cellular automaton model of investment behavior in the stock market , 2003 .

[34]  Ian T. Foster,et al.  Mapping the Gnutella Network , 2002, IEEE Internet Comput..

[35]  G. Muller,et al.  Interaction-oriented programming , 1998, Proceedings Seventh IEEE International Workshop on Enabling Technologies: Infrastucture for Collaborative Enterprises (WET ICE '98) (Cat. No.98TB100253).

[36]  Stephen Wolfram,et al.  A New Kind of Science , 2003, Artificial Life.

[37]  Jeffrey O. Kephart,et al.  Dynamic pricing by software agents , 2000, Comput. Networks.

[38]  J. Timothy Wootton,et al.  Local interactions predict large-scale pattern in empirically derived cellular automata , 2001, Nature.

[39]  Kristofer S. J. Pister,et al.  Smart Dust: Communicating with a Cubic-Millimeter Computer , 2001, Computer.

[40]  Stephen Wolfram,et al.  Cellular Automata And Complexity , 1994 .

[41]  B. Schönfisch,et al.  Synchronous and asynchronous updating in cellular automata. , 1999, Bio Systems.

[42]  Gaurav S. Sukhatme,et al.  Connecting the Physical World with Pervasive Networks , 2002, IEEE Pervasive Comput..