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Handbook of Nature-Inspired and Innovative Computing pp 77–109Cite as

Multiset Rule-Based Programming Paradigm for Soft-Computing in Complex Systems

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Abstract

This chapter describes a rule-based multiset distributed programming paradigm as a unifying theme for conventional as well as soft and innovative computing, e.g., Markov Chain Monte Carlo (MCMC)-based Bayesian inference; biological, chemical, DNA, dynamical, genetic, immuno-, and membrane computation; and nature-inspired, self-organized criticality and active walker (swarm and ant intelligence) models. The computations are interpreted as the outcome arising out of deterministic, nondeterministic, or stochastic interaction among elements in a multiset object space that includes the environment. These interactions are like chemical reactions, and the evolution of the multiset can mimic biological evolution. Since the reaction rules are inherently parallel, any number of actions can be performed cooperatively or competitively among the subsets of elements so that the elements evolve toward an equilibrium or an emergent state. Practical realization of this paradigm is achieved through a coordination programming language using Multiset and transactions. This paradigm permits carrying out parts or all of the computations independently in a distributed manner on distinct processors and is eminently suitable for cluster and grid computing. Some important applications of this paradigm are described.

Research was supported by the National Sciences and Engineering Research Council (NSERC) Canada.

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Authors and Affiliations

  1. Australian National University, Australia

    E. V. Krishnamurthy

  2. University of British Columbia, British Columbia

    Vikram Krishnamurthy

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  1. E. V. Krishnamurthy
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  2. Vikram Krishnamurthy
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  1. The University of Sydney, Australia

    Albert Y. Zomaya

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Krishnamurthy, E.V., Krishnamurthy, V. (2006). Multiset Rule-Based Programming Paradigm for Soft-Computing in Complex Systems. In: Zomaya, A.Y. (eds) Handbook of Nature-Inspired and Innovative Computing. Springer, Boston, MA. https://doi.org/10.1007/0-387-27705-6_3

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