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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
A.N. Abdallah (1995): The Logic of Partial Information, Springer Verlag, New York.
L.M. Adelman (1994): Molecular computation of solutions to combinatorial problems, Science, 266, 1021–1024.
S. Andradottir (1996): A global search method for discrete stochastic optimization, SIAM Journal of Optimization, 6, 2(1), 513–530.
S. Andradottir (1999): Accelerating the convergence of random search methods for discrete stochastic optimization, ACM Transactions on Modelling and Computer Simulation, 9, 4(1), 349–380.
R. Backhouse and J. Gibbons (2003): Generic Programming, Lecture Notes in Computer Science, Vol. 2793, Springer Verlag, New York.
J.-P. Banatre, D.L. Me’tayer (1990): The Gamma model and its discipline of programming, Science of Computer Programming, 15, 55–77.
J.-P, Banatre, D.L. Me’tayer (1993): Programming by Multiset transformation, Comm. ACM, 36, 98–111.
R.K. Belew, S. Forrest (1988): Learning and programming in classifier systems, Machine Learning 3, 193–223.
T. Blackwell and J. Branke (2004): Multi-swarm optimization in dynamic environments, Lecture Notes in Computer Science, Vol. 3005, pp. 489–500, Springer Verlag, New York.
S. Boettcher, and A. Percus (2000): Nature’s way of optimizing, Artificial Intelligence, 119, 275–286.
E. Bonabeau, M. Dorigo and G. Theraulaz (1999): Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, U.K.
L.K. Booker, D.E. Goldberg, J.H. Holland (1986): Classifier systems and Genetic Algorithms, Artificial Intelligence, 40, 235–282.
J. Branke, H.C. Andersen and H. Schmeck (1996). Global selection methods for massively parallel computers, in Evolutionary Computing, T.C. Fogarty, ed., Lecture Notes in Computer Science, 1143, 175–188, Springer Verlag, New York.
C.S. Calude, et al., (2001): Multiset processing, Lecture Notes in Computer Science, Vol. 2235, Springer Verlag, New York.
C. Cannings and D.D. Penman (2003): Models of Random graphs and their applications, Handbook of Statistics, C.R. Rao, ed., 21, 51–91, North Holland, Amsterdam.
N. Campbell (1996): Biology, Benjamin/Cummings, New York.
K.S. Chan, and H. Tong (2002): Chaos: A Statistical Perspective, Springer, New York.
S. Chu, et al., (2003): Parallel ant colony systems, Lecture Notes In Artificial Intelligence, 2871, 279–284, Springer Verlag, New York.
C.A.C. Coello, D.A. Van Veldhuizen, G.B. Lemont (2002): Evolutionary Algorithm for Solving Multi-objective Problem, Kluwer, New York.
M. Conrad (1992): Molecular computing paradigms, Computer, 25, 6–68.
M. Conrad, K.-P. Zauner (1997): Molecular computing: From conformational pattern recognition to complex processing networks, in Bioinformatics, Lecture Notes in Computer Science 1278, 1–10, Springer Verlag, New York.
M. Conrad, K-P Zauner (1998): DNA as a vehicle for the self-assembly model of computing, Biosystems, 45, 59–66.
M. Dorigo, G.D. Caro and M. Sampels (2002): Ant algorithms, Lecture Notes in Computer Science, Vol. 2463, Springer Verlag, New York.
M. Dorigo, and T. Stutzle (2004): Ant Colony Optimization, M.I.T. Press, Cambridge, Mass.
S.N. Dorogovtsev, and J.F.F. Mendes, (2003): Evolution of Networks, Oxford University Press, Oxford.
A. Doucet et al., (2000): Sequential Monte-Carlo Methods in Practice, Springer, New York.
A. Doucet, N. Gordon, V. Krishnamurthy, (2001): Particle filters for state estimation of jump Markov linear systems, IEEE Trans. Signal Processing, 49, 613–624.
J.L. Fernandez-Villacanas, J.M. Fatah, S. Amin (1998): Computing with evolving proteins, Parallel and Distributed Processing, J. Rolim, ed. Lecture Notes in Computer Science, Vol. 1388, Springer Verlag, New York, pp. 207–215.
S. Forrest (1991a): Parallelism and Programming in Classifier Systems, Morgan Kauffman, San Mateo, California.
S. Forrest (1991b): Emergent Computation, M.I.T Press, Cambridge, Mass.
M.H. Genesereth, N. Nilsson, (1987): Logical Foundations of Artificial Intelligence, Morgan Kaufmann, Los Altos, California.
D.E. Goldberg, (1989): Genetic Algorithms in Search, Optimisation and Machine Learning, Addison Wesley, Reading, Mass.
L. Goncharova, et al., (2003): Biomolecular immunocomputing, Lecture Notes in Computer Science, 2787, 102–110, Springer Verlag, New York.
J.J. Grefenstett, (1988): Credit assignment in rule discovery systems based on genetic algorithms, Machine Learning, 3, 225–245.
J.H. Holland, et al., (1987): Induction, M.I.T. Press, Cambridge, Mass.
A. Ilachinski, (2002): Cellular Automata, World Scientific, Singapore.
T. Ishida (1991): Parallel, distributed and multiagent production systems, Lecture Notes in Computer Science, 890, Springer Verlag, New York.
S.A. Kauffman (1993): The Origins of Order, Oxford University Press, Oxford.
J. Kennedy and R.C. Eberhart, (2001). Swarm Intelligence, Morgan Kauffman, London.
P. Kevin MacKeown (1997): Stochastic Simulation in Physics, Springer, New York.
P.M Kogge, (1991): The Architecture of Symbolic Computers, McGraw Hill, New York.
J.R. Koza, (1994): Genetic Programming II, M.I.T. Press, Cambridge, Mass.
E.V. Krishnamurthy, (1985): Introductory Theory of Computer Science, Springer Verlag, New York.
E.V. Krishnamurthy (1986): Solving problems by random trials, Science and computers, (A volume dedicated to Nicholas Metropolis), G.C. Rota, ed., Advances in Mathematics, 10, 61–81, Academic Press, New York.
E.V. Krishnamurthy, (1989): Parallel Processing, Addison Wesley, Reading, Mass.
E.V. Krishnamurthy (1996): Complexity issues in parallel and distributed computing, in Handbook of Parallel and Distributed Computing, Chapter 4, A. Zomaya, ed., McGraw Hill, New York.
E.V. Krishnamurthy, (2003): Algorithmic entropy, phase transitions, and smart systems, Lecture Notes in Computer Science, 2659, 333–342, Springer Verlag, New York.
E.V. Krishnamurthy, (2004): Rule-based Multiset Programming Paradigm, Applications to Synthetic Biology, Third Workshop on Non-Silicon Computation, (NSC-3), Munich, in 31st International Symposium on Computer Architecture, Munich, June 2004.
E.V. Krishnamurthy, V.K. Murthy, (1992): Transaction Processing Systems, Prentice Hall, Sydney.
V. Krishnamurthy, and E.V. Krishnamurthy, (1999): Rule-based Programming Paradigm: A formal basis for biological, chemical and physical computation, Biosystems, 49, 205–228.
E.V. Krishnamurthy, and V. Krishnamurthy (2001): Quantum field theory and computational paradigms, International Journal of Modern Physics, 12C, 1179–1201.
V. Krishnamurthy, and S.H. Chung (2003): Adaptive learning algorithms for Nernst potential and I-V curves in nerve cell membrane ion channels modeled as hidden Markov models, IEEE Transactions NanoBioScience, 2(4), 266–278.
V. Krishnamurthy, X. Wang, G. Yin (2004): Adaptive Spreading Code Optimization and Adaptation in CDMA via Discrete Stochastic Approximation, IEEE Transactions Information Theory, 50(9), 1927–1949.
I. M. Kulic (1998): Evaluating polynomials on the molecular level—a novel approach to molecular computers, Biosystems, 45, 45–57.
S. Kuo, D. Moldovan, (1992): The state of the art in parallel production systems, J. Parallel and Distributed Computing, 15, 1–26.
L. Lam (1998): Nonlinear Physics for Beginners, World Scientific, Singapore.
A.J. Lichtenberg and M.A. Liberman, (1983): Regular and Stochastic Motion, Springer Verlag, New York.
R.J. Lipton (1995): DNA solution to hard computational problems, Science, 268, 542–545.
W. Ma, E.V. Krishnamurthy and V.K. Murthy (1995): Multran—A coordination programming language using multiset and transactions, Proc. Neural, Parallel and Scientific Computing, 1, 301–304, Dynamic Publishers, Inc., U.S.A.
N. Meuleau and M. Dorigo, (2002): Ant colony optimization and stochastic gradient descent, Artificial Life, 8, 103–121.
Z. Michalewicz (1992): Genetic Algorithms + Data Structures = Evolution Programs, Springer Verlag, New York.
Z. Michalewicz and D.B. Fogel (2000): How to Solve It: Modern Heuristics, Springer Verlag, New York. (1992
D. Midgley (2003): Systems Thinking, Vols. 1–4, Sage Publications, London.
R.K. Milne (2001): Point processes and some related processes, Handbook of Statistics, 19, 599–641, C.R. Rao, ed., North Holland, Amsterdam.
D.P. Miranker (1991), TREAT: A New Efficient Match Algorithm for AI Production Systems, Pitman, London.
B. Misra, I. Prigogine and M. Courbage (1979), From deterministic dynamics to probabilistic descriptions, Physica, 98A, 1–26.
R. Motwane and P. Raghavan (1995), Randomized Algorithms, Cambridge University Press, Cambridge.
H. Muehlenbein (1991), Evolution in time and space-the parallel genetic algorithm, in Foundations of Genetic algorithms, Rawlins, G., ed., Morgan Kaufmann, San Mateo, California, 316–337.
J.D. Murray (2003): Mathematical Biology, Springer, New York.
V.K. Murthy and E.V. Krishnamurthy (1995): Probabilistic Parallel Programming based on multiset transformation, Future Generation Computer Systems, 11, 283–295.
V.K. Murthy and E.V. Krishnamurthy, (2003): Entropy and Smart systems, International Journal of Smart Engineering Systems, 5, 481–499.
K.M. Pacino (2002): Biomimicry of bacterial foraging for distributed optimization and control, IEEE Control magazine, 22(3), 52–68.
C.H. Papadimitriou (1985): Computational Complexity, Addison Wesley, Reading, Mass.
G. Paun (2003): Membrane computing, Lecture Notes in Computer Science, FCT 2003, 2751, 284–295, Springer Verlag, New York.
D. Petrina, Ya., (1995): Mathematical Foundations of Quantum Statistical Mechanics, Kluwer Academic Publishers, London.
I. Prigogine (1980): From Being to Becoming, W.H. Freeman, San Fransisco.
N.G. Rambidi (1997): Biomolecular computer: roots and promises, Biosystems, 44, 1–15.
R.D. Reiss (1993): A Course on Point processes, Springer Verlag, New York.
C.P. Robert and G. Casella (1999) Monte Carlo Statistical Methods, Springer Verlag.
E. Rich, K. Knight (1991): Artificial Intelligence, McGraw Hill, New York.
J.D. Scargle and G.J. Babu (2003), Point processes in astronomy, Handbook of Statistics, C.R. Rao, ed., 21, 795–825, North Holland, Amsterdam.
R.J. Solomonoff (1995): The discovery of algorithmic probability: A guide for the programming of true creativity, Lecture Notes in Computer Science, 904, 1–22.
J.C. Spall (2003): Introduction to Stochastic Search and Optimization, Wiley-Interscience, New York.
W.M. Spears, and K.A. De Jong (1993): An overview of evolutionary computation, Machine Learning ECLML-93, Lecture Notes in Computer Science, 667, 442–459, Springer Verlag, New York.
S. Stepney, J.A. Clark et al., (2003): Artificial Immune System and the grand challenges for non-classical computation, Lecture Notes in Computer Science, 2787, 204–216, Springer Verlag, New York.
D. Straub (1997): Alternative Mathematical Theory of Nonequilibrium Phenomena, Academic Press, New York.
Y. Suzuki, et al., (2001): Artificial Life applications of a class of P systems: Abstract rewriting systems on Multisets, Lecture Notes in Computer Science, 2235, 299–346, Springer Verlag, New York.
A.M. Turing (1952): The chemical basis for morphogenesis, Phil. Trans. Roy. Soc. London, 237, 37–79.
W. Wayt Gibbs (2004): Synthetic life, Scientific American, 290(5), 48–55.
D. Whitley T. Starkweather (1990): Genitor: a distributed Genetic algorithm, J. Experimental and Theoretical Artificial Intelligence, 2, 184–214.
S. Wolfram (2002): A New Kind of Science, Wolfram Media Inc., Champaign, Ill.
X. Yao, (2003): The evolution of evolutionary computation, Lecture Notes in Artificial Intelligence, 2773, 19–20, Springer Verlag, New York.
G. Yin, V. Krishnamurthy and C. Ion (2004): Regime Switching Stochastic Approximation Algorithms with application to adaptive discrete stochastic optimization, SIAM Journal of Optimization, 14(4), 1187–1215.
D.C.K. Yuen and B.A. MacDonald (2004): Theoretical considerations of multiple particle filters for simultaneous localization and map-building, Lecture Notes in Computer Science, 3213, 203–209.
K.-P. Zauner, M. Conrad (1996): Parallel computing with DNA: toward the Anti-Universal Machine, Proc. PPSN-IV, Lecture Notes in Computer Science, 1141, Springer Verlag, New York.
W. Zhang and R. Korf (1996): A study of complexity transitions on the asymmetric travelling salesman problem, Artificial Intelligence, 81, 223–239.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer Science+Business Media, Inc.
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/0-387-27705-6_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-40532-2
Online ISBN: 978-0-387-27705-9
eBook Packages: Computer ScienceComputer Science (R0)