Interacting Agents in a Network for in silico Modeling of Nature-Inspired Smart Systems

An interacting multi-agent system in a network can model the evolution of a Nature-Inspired Smart System (NISS) exhibiting the four salient properties: (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. The quantitative understanding of a Smart system with an arbitrary interactive topology is extremely difficult. However, for specific applications and a pre-defined static interactive topology among the agents, the quantitative parameters can be obtained through simulation to build a specific NISS. Further developments of agent technology will be of great value to model, simulate and animate, many phenomena in Systems biology pattern formation, cellular dynamics, cell motility, growth and development biology, 1 *in silico = In or by means of a computer simulation; in silico is closely based on two older Latin phrases that are key terms in the jargon of every biologist and biochemist: in vivo and in vitro, both of which came into use at the end of the nineteenth century. The first translates as “in that which is alive”, and refers to some experiment carried out within a living organism, such as a drug test on an animal. The second means “in glass” and is used for experiments that take place in an artificial environment, such as a test tube or culture dish (WIKIPEDIA) V.K. Murthy and E.V. Krishnamurthy: Interacting Agents in a Network for in silico Modeling of www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007 Melbourne, Victoria 3000, Australia. kris.murthy@rmit.edu.au Nature-Inspired Smart Systems, Studies in Computational Intelligence (SCI) 72, 177–231 (2007) and can provide for improved capability in complex systems modelling. Also agents will serve as useful tools to model, design and develop biomorphic robots and neuromorphic chips.

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