Can Artificial Life Emerge in a Network of Interacting Agents?

An interacting multi-agent system in a network can behave like a nature-inspired smart system (SS) exhibiting the four salient properties of an artificial life system (ALS): (i) Collective, coordinated and efficient (ii) Self-organization and emergence (iii) Power law scaling or scale invariance under emergence (iv) Adaptive, fault tolerant and resilient against damage. We explain how these basic properties can arise among agents through random enabling, inhibiting, preferential attachment and growth of a multiagent system. However,the quantitative understanding of a Smart system with an arbitrary interactive topology is extremely difficult. Hence we cannot design a general purpose programmable Smart system. However, for specific applications and a predefined static interactive topology among the agents, the quantitative parameters can be obtained through simulation to build a specific SS.

[1]  George M. Zaslavsky,et al.  Chaotic Dynamics and the Origin of Statistical Laws , 1999 .

[2]  Ilya Prigogine,et al.  From Being To Becoming , 1980 .

[3]  Ralph C. Smith.  Smart Material Systems , 2005 .

[4]  Vikram Krishnamurthy,et al.  Multiset Rule-Based Programming Paradigm for Soft-Computing in Complex Systems , 2006, Handbook of Nature-Inspired and Innovative Computing.

[5]  S. Torquato.  Random Heterogeneous Materials , 2002 .

[6]  H. Crichton-Miller.  Adaptation , 1926 .

[7]  Werner Ebeling,et al.  Self-Organization, Active Brownian Dynamics, and Biological Applications , 2002, cond-mat/0211606.

[8]  A. Vulpiani,et al.  Kolmogorov’s Legacy about Entropy, Chaos, and Complexity , 2003 .

[9]  Giovanna Di Marzo Serugendo,et al.  Engineering Emergent Behaviour: A Vision , 2003, MABS.

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

[11]  Ralph C. Smith,et al.  Smart material systems - model development , 2005, Frontiers in applied mathematics.

[12]  Steven H. Strogatz,et al.  Sync: The Emerging Science of Spontaneous Order , 2003 .

[13]  J. van Leeuwen,et al.  Ant Algorithms , 2002, Lecture Notes in Computer Science.

[14]  Michail Zak,et al.  From instability to intelligence , 1997 .

[15]  David Strauss.  On a general class of models for interaction , 1986 .

[16]  Y. Shoham.  Introduction to Multi-Agent Systems , 2002 .

[17]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[18]  Francis C. Moon,et al.  Chaotic and fractal dynamics , 1992 .

[19]  Gesine Reinert,et al.  Small worlds , 2001, Random Struct. Algorithms.

[20]  Shlomo Havlin,et al.  Fractals in Science , 1995 .

[21]  Mauro Birattari,et al.  Toward the Formal Foundation of Ant Programming , 2002, Ant Algorithms.

[22]  S. N. Dorogovtsev,et al.  Evolution of networks , 2001, cond-mat/0106144.

[23]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[24]  Robert C. Hilborn,et al.  Chaos and Nonlinear Dynamics , 2000 .

[25]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[26]  Guy Theraulaz,et al.  Self-Organization in Biological Systems , 2001, Princeton studies in complexity.