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Future Directions of Applied Computational Intelligence

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Abstract

The objective of the last chapter of the book is to estimate the future trends and areas of applied computational intelligence. It includes analysis of two different trends. The first trend focuses on the next generation of computational intelligence technologies, which are still in the research domain, but demonstrate potential for future industrial applications. Examples are technologies such as computing with words, evolving intelligent systems, co-evolution, and artificial immune systems. The second trend explores the projected needs in industry for the next 10–15 years, such as predictive marketing, accelerated new products diffusion, high-throughput innovation, etc. The key assumption is that the expected industrial needs will drive the development and deployment of emerging technologies, like computational intelligence. A method that describes the principles and mechanisms of applied research driven by industrial demand is discussed in this chapter. Fortunately, almost any existing and new computational intelligence methods may contribute to satisfying current and future industrial demand. However, the long-term sustainability of computational intelligence depends on broadening the application audience from large corporations to small businesses and individual users.

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Notes

  1. 1.

    An interesting reference on using generic trends in long-term forecasts is the visionary book of R. Kurzweil, The Singularity is Near: When Humans Transcend Biology, Viking, 2005. An opposite approach of predictions based on different microtrends is presented in the book of M. Penn and K. Zalesne, Microtrends: The Small Forces Behind Tomorrow's Big Changes, Twelve, New York, NY, 2007.

  2. 2.

    J. Ellenberg, The Netflix challenge, Wired, March 2008, pp. 114–122, 2008

  3. 3.

    http://www.darpa.mil/grandchallenge/

  4. 4.

    A description of the Tennessee-Eastman problem and an archive of models can be found in http://depts.washington.edu/control/LARRY/TE/download.html

  5. 5.

    The state of the art of this technology is given in the paper: L. Zadeh, Toward human level machine intelligence – Is it achievable? The need for a paradigm shift, IEEE Computational Intelligence Magazine, 3, 3, pp. 11–22, 2008.

  6. 6.

    L. Zadeh, Fuzzy logic = Computing with words, IEEE Trans. Fuzzy Systems, 90, pp. 103–111, 1996.

  7. 7.

    L. Zadeh, Precisiated natural language (PNL), AI Magazine, 25, pp. 74–91, 2004.

  8. 8.

    S. Aja-Fernandez and C. Alberola-Lopez, Fuzzy granules as a basic word representation for computing with words, SPECOM 2004, St. Petersburg, Russia, 2004.

  9. 9.

    J. Mendel, An architecture for making judgment using computing with words, Int. J. Appl. Math. Comput. Sci, 12, pp. 325–335, 2002.

  10. 10.

    A good example of using computing with words in time series analysis is the chapter of J. Kacprzyk, A. Wilbik, and S. Zadrozny, Towards human consistent linguistic summarization of time series via computing with words and perceptions, in Forging New Frontiers: Fuzzy Pioneers 1, M. Nikravesh, J. Kacprzyk, and L. Zadeh (Eds), Springer, pp. 17–35, 2007.

  11. 11.

    P. Angelov, http://www.scholarpedia.org/article/Evolving_fuzzy_systems

  12. 12.

    P. Angelov, A. Kordon, and X. Zhou, Evolving fuzzy inferential sensors for process industry, 3 rd Workshop on Genetic and Evolving Fuzzy Systems, Witten-Bommerholz, Germany, pp. 41–46, 2008.

  13. 13.

    D. Filev and F. Tseng, Novelty detection-based machine health prognostics, In Proc. 2006 International Symposium on Evolving Fuzzy Systems, IEEE Press, pp. 193–199, 2006.

  14. 14.

    E. de Jong, K. Stanley, R. P. Wiegand, Introductory tutorial on co-evolution, Proceedings of GECCO 2007, London, UK, pp. 3133–3157, 2007.

  15. 15.

    M. Mitchell, M. Thomure, and N. Williams, The role of space in the success of co-evolutionary learning. In Proceedings of Artificial Life X: Tenth Annual Conference on the Simulation and Synthesis of Living Systems, MIT Press, 2006.

  16. 16.

    H. Chen, K. Wong, D. Nguyen, and C. Chung. Analyzing oligopolistic electricity market using co-evolutionary computation. IEEE Transactions on Power Systems, 21, pp. 143–152, 2006.

  17. 17.

    B. LeBaron. Financial market efficiency in a co-evolutionary environment. In Proceedings of the Workshop on Simulation of Social Agents: Architectures and Institutions, pp. 33–51. Argonne National Laboratory and the University of Chicago, 2001.

  18. 18.

    L. de Castro and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, Springer, 2002.

  19. 19.

    L. de Castro and F. J. von Zuben, The clonal selection algorithm with engineering applications, Proc. GECCO Workshop on Artif. Immune Syst. Their Appl., pp. 36–37, 2000.

  20. 20.

    A representative survey of the state of the art and the selected example are from the paper: D. Dasgupta. Advances in artificial immune systems. IEEE Computational Intelligence Magazine, 1, 5, pp. 40–49, 2006.

  21. 21.

    http://www.nasa.gov/vision/earth/technologies/mildsoftware_jb.html

  22. 22.

    The material in this section was originally published in: A. Kordon, Soft computing in the chemical industry: Current state of the art and future trends, In: Forging the New Frontiers: Fuzzy Pioneers I: Studies in Fuzziness and Soft Computing, M. Nikravesh, J. Kacprzyk, and L. A. Zadeh (eds), pp. 397–414, Springer, 2007. With kind permission of Springer-Verlag.

  23. 23.

    D. Schwartz, Concurrent marketing analysis: A multi-agent model for product, price, place, and promotion, Marketing Intelligence & Planning , 18(1), pp. 24–29, 2000.

  24. 24.

    B. Carlsson, S. Jacobsson, M. Holmén, and A. Rickne, Innovation systems: Analytical and methodological issues, Research Policy, 31, pp. 233–245, 2002.

  25. 25.

    J. Cawse (Editor), Experimental Design for Combinatorial and High-Throughput Materials Development, John Wiley, 2003.

  26. 26.

    A. Conradie, R. Miikkulainen, and C. Aldrich, Adaptive control utilising neural swarming, Proceedings of GECCO 2002, New York, NY, pp. 60–67, 2002.

  27. 27.

    A. Conradie and C. Aldrich, Development of neurocontrollers with evolutionary reinforcement learning, Computers and Chemical Engineering, 30 (1), pp. 1–17, 2006.

  28. 28.

    J. Shapiro, Modeling the Supply Chain, Duxbury, Pacific Grove, CA, 2001.

  29. 29.

    Y. Li, K. Ang, and G. Chong, Patents, software, and hardware for PID control, IEEE Control Systems Magazine, 26 (1), pp. 42–54, 2006.

Suggested Reading

  • The following references describe some of the new computational intelligence techniques:

    Google Scholar 

  • P. Angelov, D. Filev, and N. Kasabov (Eds), Evolving Intelligent Systems: Methodology and Applications, Wiley, in press.

    Google Scholar 

  • D. Dasgupta, Advances in Artificial Immune Systems: IEEE Computational Intelligence Magazine, 1, 5, pp. 40–49, 2006.

    Google Scholar 

  • L. de Castro and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, Springer, 2002.

    MATH  Google Scholar 

  • L. Zadeh and J. Kacprzyk (Eds), Computing with Words in Information/Intelligent Systems 1(Foundations), Springer, 1999.

    MATH  Google Scholar 

  • L. Zadeh, Toward human level machine intelligence – Is it achievable? The need for a paradigm shift, IEEE Computational Intelligence Magazine, 3, 3, pp. 11–22, 2008.

    Article  Google Scholar 

  • A good reference on using generic trends in long-term forecasts is the visionary book of R. Kurzweil, The Singularity is Near: When Humans Transcend Biology, Viking, 2005.

    Google Scholar 

  • An opposite approach to predictions based on different micro-trends is presented in the book of M. Penn and K. Zalesne, Microtrends: The Small Forces Behind Tomorrow's Big Changes, Twelve, New York, NY, 2007.

    Google Scholar 

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Authors and Affiliations

  1. Data Mining and Modeling Capability Corporate R&D, The Dow Chemical Company, 2301 N. Brazosport Blvd., B-1226, Freeport, TX, 77541, USA

    Arthur K. Kordon

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  1. Arthur K. Kordon
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Correspondence to Arthur K. Kordon .

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Kordon, A.K. (2010). Future Directions of Applied Computational Intelligence. In: Applying Computational Intelligence. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69913-2_15

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  • DOI: https://doi.org/10.1007/978-3-540-69913-2_15

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