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Distributed Computation Multi-agent System

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Abstract

This article addresses a formal model of a distributed computation multi-agent system. This model has evolved from the experimental research on using multi-agent systems as a ground for developing fuzzy cognitive maps. The main paper contribution is a distributed computation multi-agent system definition and mathematical formalization based on automata theory. This mathematical formalization is tested by developing distributed computation multi-agent systems for fuzzy cognitive maps and artificial neural networks – two typical distributed computation systems. Fuzzy cognitive maps are distributed computation systems used for qualitative modeling and behavior simulation, while artificial neural networks are used for modeling and simulating complex systems by creating a non-linear statistical data model. An artificial neural network encapsulates in its structure data patterns that are hidden in the data used to create the network. Both of these systems are well suited for formal model testing. We have used evolutionary incremental development as an agent design method which has shown to be a good approach to develop multi-agent systems according to the formal model of a distributed computation multi-agent system.

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References

  1. Ferver J.: Multi-agent Systems, An Introduction to Distributed Artificial Intelligence. Addison-Wesley, England (1999)

    Google Scholar 

  2. Weiss, G. (Ed.), Multiagent Systems, The MIT Press, Cambridge, Massachusetts, 1999.

  3. Wooldridge, M., Jennings, N., “Intelligent Agents: Theory and Practice,” Knowledge Engineering Review, 10, 2, pp. 115–152.

  4. Jennings, N. R., “On agent-based software engineering,” Artificial Intelligence, 117, pp. 277–296, 2000.

  5. Braubach, L., Pokahr, A., Bade, D., Krempels, K. H., Lamersdorf, W., “Deployment of Distributed Multi-agent Systems,” Fifth International Workshop Engineering Societies in the Agents, pp. 335–347, 2005.

  6. Brinn, M., Berliner, J., Helsinger, A., Wright, T., Dyson, M., Rho, S., Wells, D., “Extending the Limits of DMAS Survivability: The UltraLog Project,” IEEE Intelligent Systems, 19, 5, pp. 53–61, 2004.

    Google Scholar 

  7. Gil, Y., “On agents and grids: Creating the fabric for a new generation of distributed intelligent systems,” Web Semantics: Science, Services and Agents on the World Wide Web, 4, 2, pp. 116–123, 2006.

  8. Ojha, A.C., Pradhan, S. K., Patra, M. R., “Distributed Multi-agent System Architecture for Mobile Traders,” Proc. on ICIT, pp. 214–216, 2007.

  9. Woodridge M.: An Introduction to MultiAgent Systems. John Wiley & Sons, New York (2002)

    Google Scholar 

  10. Zhong, Z., McCalley, J. D., Vishwanathan, V., Honavar, V., “Multiagent system solutions for distributed computing, communications, and data integration needs in the power industry,” IEEE Power Engineering Society General Meeting, 1, pp. 45–49, 2004.

  11. Kosko, B., “Fuzzy cognitive maps,” International Journal Man-Machine Studies, 24, pp. 65–75, 1986.

  12. Kandasamy, W.B.V., Smarandache, F., Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps, Books on Demand, ProQuest Information & Learning, USA, 2003.

  13. Jennings, N. R., Sycara, K., Wooldridge, M., “A Roadmap of Agent Research and Development,” Autonomous Agents and Multi-Agent Systems, 1, pp. 275–306, 1998.

  14. Russell S., Norvig P.: Artificial Intelligence: A Modern Approach, Prentice Hall. Upper Saddle River, USA (2000)

    Google Scholar 

  15. Gruber, T. R., “Toward Principles for the Design of Ontologies Used for Knowledge Sharing,” International Journal of Human Computer Studies, 43, 5-6, pp. 907–928, 1995.

  16. Jennings, B., Brennan, R., Gustavsson, R., Feldt, R., Pitt, J., Prouskas, K., Quantz, J., “FIPA-compliant agents for real-time control of Intelligent Network traffic,” Computer Networks, 31, 19, pp. 2017–2036.

  17. Wang, S., Xi, L., Zhou, B., “FBS-enhanced agent-based dynamic scheduling in FMS,” Engineering Applications of Artificial Intelligence, 21, 4, pp. 644–657, 2008.

  18. Foundation for Intelligent Physical Agents (FIPA), SC00061G FIPA ACL Message Structure Specification, Geneva, Switzerland, 2002.

  19. Foundation for Intelligent Physical Agents (FIPA), SC00001L FIPA Abstract Architecture Specification, Geneva, Switzerland, 2002.

  20. Bellifemine, F., Caire, G., Rimassa, G., Poggi, A., “JADE: A software framework for developing multi-agent applications. Lessons learned,” Information and Software Technology, 50, 12, pp. 10–21, 2008.

  21. Bellifemine, F., Rimassa, G., Poggi, A., “JADE - A FIPA-Compliant Agent Framework,” Proc. of the 4th Int. Conf. and Exhibition on the Practical Application of Intelligent Agents and Multi-Agents, UK, 1999.

  22. Stylios, C. D., Groumpos, P. P., “Fuzzy Cognitive Map in Modeling Supervisory Control Systems,” Journal of Intelligent & Fuzzy Systems, 8, 2, pp. 83–98, 2000.

    Google Scholar 

  23. Koulouriotis, D. E., Diakoulakis, I. E., Emiris, D. M., Antonidakis, E. N., Kaliakatsos, I. A., “ Efficiently modeling and controlling complex dynamic systems using evolutionary fuzzy cognitive maps,” International Journal of Computational Cognition, 1, 2, pp. 41–65, 2003.

    Google Scholar 

  24. Štula, M., Stipaničev, D., Bodrozic, L., “Intelligent Modeling with Agent- Based Fuzzy Cognitive Map,” International Journal of Intelligent Systems, 25, pp. 981–1004, 2010.

    Google Scholar 

  25. Brooks, R. A., “Intelligence without representation,” Artificial Intelligence, 47, pp. 139–159, 1991.

  26. Muller J. P.: “The Design of Intelligent Agents: A Layered Approach,” Lecture Notes in Computer Science. Springer, New York, USA (1996)

    Google Scholar 

  27. Pressman R. S.: Software Engineering: A Practitioner’s Approach. McGraw-Hill, New York, USA (2001)

    Google Scholar 

  28. Bellifemine F., Caire G., Greenwod D.: Developing Multi-Agent Systems with JADE. John Wiley & Sons, New York, USA (2007)

    Book  Google Scholar 

  29. McCulloch, W. S. and Pitts, W., “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biophysics, 5, pp. 115–133, 1943.

  30. Minsky M., Papert S.: Perceptrons. The MIT Press, Cambridge (1969)

    MATH  Google Scholar 

  31. Hecht-Nielsen, R., “Neural Network Primer: Part I.,” AI Expert, pp. 4–51, 1989.

  32. Haykin S.: Neural Networks: Comprehensive Foundation. Macmillan, New York, USA (1994)

    MATH  Google Scholar 

  33. Nigrin A.: Neural Networks for Pattern Recognition. The MIT Press, A Bradford Book, USA (1993)

    MATH  Google Scholar 

  34. Kawato, M., “Computational Schemes and Neural Network Models for Formation and Control of Multijoint Arm Trajectory,” Neural Networks for Control (Miller, W.T., Sutton, R. S. and Werbos, P.J., Eds.), The MIT Press, Boston, pp. 197–228, 1990.

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Correspondence to Maja Štula.

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Štula, M., Stipaničev, D. & Maras, J. Distributed Computation Multi-agent System. New Gener. Comput. 31, 187–209 (2013). https://doi.org/10.1007/s00354-012-303-8

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