Ordered asynchronous processes in multi-agent systems

Models of multi-agent systems usually update the states of all agents synchronously, but in many real life systems, agents behave asynchronously. Relatively little is yet known about the dynamic characteristics of asynchronous systems. Here we compare synchronous, random asynchronous, and ordered asynchronous updating schemes. Using one-dimensional (1D) cellular automata as a case study, we show that the type of update scheme strongly affects the dynamic characteristics of the system. We also show that global synchronisation can arise from local temporal coupling. Furthermore, it is possible to switch between chaotic, cyclic and modular behaviour by varying a single parameter, which suggests a possible mechanism by which environmental parameters influence emergent structure. We conclude that ordered asynchronous processes with local temporal coupling play a role in self-organisation within many multi-agent systems.

[1]  Roger F. Malina,et al.  Hidden Order: How Adaptation Builds Complexity by John H. Holland (review) , 2017 .

[2]  Christopher G. Langton,et al.  Computation at the edge of chaos: Phase transitions and emergent computation , 1990 .

[3]  Ivan E Sutherland,et al.  Computers without clocks. , 2002, Scientific American.

[4]  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 .

[5]  Yasusi Kanada,et al.  The Effects of Randomness in Asynchronous 1D Cellular Automata , 1984 .

[6]  Marco Tomassini,et al.  Evolving Asynchronous and Scalable Non-uniform Cellular Automata , 1997, ICANNGA.

[7]  Pekka Orponen,et al.  Computing with Truly Asynchronous Threshold Logic Networks , 1997, Theor. Comput. Sci..

[8]  Stephen Wolfram,et al.  Cellular automata as models of complexity , 1984, Nature.

[9]  Steven H. Low,et al.  Optimization flow control—I: basic algorithm and convergence , 1999, TNET.

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

[11]  J Delgado,et al.  Self-synchronization and task fulfilment in ant colonies. , 2000, Journal of theoretical biology.

[12]  D. G. Green.  Shapes of simulated fires in discrete fuels , 1983 .

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

[14]  Walter J. Freeman,et al.  TUTORIAL ON NEUROBIOLOGY: FROM SINGLE NEURONS TO BRAIN CHAOS , 1992 .

[15]  Felisa J. Vázquez-Abad,et al.  Centralized and decentralized asynchronous optimization of stochastic discrete-event systems , 1998 .

[16]  S. Strogatz.  From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators , 2000 .

[17]  Nigel R. Franks,et al.  Synchronization of the behaviour within nests of the ant Leptothorax acervorum (fabricius)—I. Discovering the phenomenon and its relation to the level of starvation , 1990 .

[18]  P. H. Kourtz,et al.  A Model a Small Forest Fire ... to Simulate Burned and Burning Areas for Use in a Detection Model , 1971 .

[19]  B. Cole.  Short-Term Activity Cycles in Ants: Generation of Periodicity by Worker Interaction , 1991, The American Naturalist.

[20]  David G. Green,et al.  Ordered Asynchronous Processes In Natural And Artificial Systems , 2001 .

[21]  I. Noble,et al.  The Use of Vital Attributes to Predict Successional Changes in Plant Communities Subject to Recurrent Disturbances , 1980 .

[22]  Ronald A. Howard,et al.  Dynamic Probabilistic Systems , 1971 .

[23]  John H. Holland,et al.  Hidden Order: How Adaptation Builds Complexity , 1995 .