An Introduction to Contemporary Achievements in Intelligent Systems

  • Jeffrey W. Tweedale
  • Ivan Jordanov
Part of the Studies in Computational Intelligence book series (SCI, volume 442)


The term intelligent systems is used to describe the necessary level of performance required to achieve the system goals. Intelligence has been observed and scientifically categorized as a biological stimuli response mechanism that is provided to satisfy an intended activity.Intelligence considers cognitive aspects of human behaviour, such as perceiving, reasoning, planning, learning, communicating and innovation. As society evolved, innovative individuals invented tools to assist them in achieving better outcomes. Since the industrial revolution [1], science and mechanization have become central to many academic challenges, driving a paradigm shift from philosophy towards systems engineering techniques. This desire to improve mechanized systems created the need for improvements to automation processes. These achievements extend the pioneering efforts of others stimulating new research and developments [2]. Computational Intelligence(CI) has evolved over the past 60 years [3] with many new fields of study emerging to dissolve obstacles encountered. These attempts relate to efforts at personifying attributes of human behavior and knowledge processes within machines. The resulting Machine Intelligence [4,5] efforts stimulated the study of Artificial Intelligence(AI) [6,7] and led to the evolution of many contemporary techniques.


Intelligent System Fuzzy Neural Network Nonlinear Activation Function American Heritage Dictionary John McCarthy 
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.


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  1. 1.
    Hudson, P.: The Industrial Revolution. Oxford University Press, Carey (1992)Google Scholar
  2. 2.
    Leedy, D.P., Ormrod, J.E.: Practical Research: Planning and Design, 8th edn. Person Press, New Jersey (2001)Google Scholar
  3. 3.
    Andresen, S.L.: John McCarthy: Father of AI. IEEE Intelligent Systems 17, 84–85 (2002)CrossRefGoogle Scholar
  4. 4.
    Friedberg, R.M.: A learning machine: part I. IBM J. Res. Dev. 2, 2–13 (1958)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Friedberg, R.M., Dunham, B., North, J.H.: A learning machine: part II. IBM J. Res. Dev. 3, 282–287 (1959)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Minsky, M.: Heuristic aspects of the artificial intelligence problem. Lincoln Laboratory Report, Federal Scientific and Technical Information, Dept. of Commerce, Washington, pp. 34–55 (1956)Google Scholar
  7. 7.
    Russel, S., Norvig, P. (eds.): Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall Series in Artificial Intelligence. Prentice Hall (2003)Google Scholar
  8. 8.
    Wooldridge, M., Jennings, N.R.: The cooperative problem-solving process. Journal of Logic and Computation 9, 563–592 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Grevier, D.: AI–The Tumultuous History of the Search for Artificial Intelligence. Basic Books, New York (1993)Google Scholar
  10. 10.
    Minsky, M.: Society of Mind. Simon and Schuster, Pymble (1985)Google Scholar
  11. 11.
    Thagard, P.R.: Computational Philiosphy of Science. MIT Press (1993)Google Scholar
  12. 12.
    Franklin, S., Graesser, A.: Is it an agent, or just a program?: A taxonomy for autonomous agents. In: Proceedings of the Third International Workshop on Agent Theories, Architectures and Languages, Budapest, Hungary, pp. 193–206 (1996)Google Scholar
  13. 13.
    Tweedale, J., Jain, L.: The evolution of intelligent agents within the world wide web. In: Nguyen, N., Jain, L. (eds.) Intelligent Agents in the Evolution of Web and Applications. SCI, vol. 167, pp. 1–9. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Tweedale, J., Jain, L.C.: Embedded Automation in Human-Agent Environment. Adaptation, Learning, and Optimization, vol. 10. Springer, Heidelberg (2011)Google Scholar
  15. 15.
    Soanes, C., Stevenson, A.: Concise Oxford English dictionary. Oxford University Press, New York (2004)Google Scholar
  16. 16.
    Harcourt, A., Brace, D. (eds.): The American Heritage Dictionary of the English Language, 5th edn. Houghton Mifflin, Boston (2011)Google Scholar
  17. 17.
    Krishnakumar, K.: Intelligent systems for aerospace engineering - an overview. Technical Report ADA484100, NASA AMES Research Center, Mountain View, CA (2003)Google Scholar
  18. 18.
    J7, J.D.D. (ed.): DoD Dictionary of Military and Associated Terms. Number JP 102, US Joint Staff, Washington DC (June 2003)Google Scholar
  19. 19.
    McCarthy, J.: Programs with common sense. In: Symposium on Mechanization of Thought Processes, Teddington, England. National Physical Laboratory (1958)Google Scholar
  20. 20.
    McCorduck, P.: Machines who think, pp. 1–375. Freeman, San Francisco (1979)Google Scholar
  21. 21.
    Newell, A., Simon, H.A.: Human Problem Solving. Prentice-Hall, Englewood Cliffs (1972)Google Scholar
  22. 22.
    Newell, A.: Production systems: Models of control structure. In: Chase, W.G. (ed.) Visual and Information Processing, pp. 463–526. Academic Press, San Diego (1973)CrossRefGoogle Scholar
  23. 23.
    French, R.M.: The chinese room: Just say “no!”. In: Gleitman, L.R., Joshi, A.K. (eds.) 22nd Annual Cognitive Science Society Conference. Institute of Research and Cognitive Science, pp. 657–662. Lawrence Erlbaum Assoc., NJ (2000)Google Scholar
  24. 24.
    Vaux, J., Dale, R.: Review of “mind over machine”. In: AI & Society, vol. 1(1), pp. 72–76. Springer, New York (1987)Google Scholar
  25. 25.
    Fredholm, L.: Pavlov’s Dog. Nobel Media, Stockholm (2001)Google Scholar
  26. 26.
    Ericsson, K.A.: The Cambridge handbook of expertise and expert performance. Cambridge University Press, New York (2006)CrossRefGoogle Scholar
  27. 27.
    Descartes, R.: Meditation vi. In: Cottingham, J. (ed.) Meditations on the First Philosophy (Translated 1986). Cambridge University Press (1641)Google Scholar
  28. 28.
    Grey, W.W.: The Living Brain. Duckworth (1953)Google Scholar
  29. 29.
    Rosen, C.A., NiIsson, N.J., Adams, M.B.: A research and development program in applications of intelligent automata to reconnaissance. Proposal for Research ESU 65-1, Stanford Research Institute, Menlo Park, California (1965)Google Scholar
  30. 30.
    Raphael, B.: Robot research at stanordresearch institute. Technical Note 64, Stanford Research Institute, Menlo Park, California (1972)Google Scholar
  31. 31.
    Hayward, M.: A connectionist model of poetic meter. In: Dowd, T., Janssen, S. (eds.) Poetics, vol. 20(4), pp. 303–317. Elsevier Press, New York (1991)Google Scholar
  32. 32.
    Tweedale, J., Ichalkaranje, N., Sioutis, C., Jarvis, B., Consoli, A., Phillips-Wren, G.: Innovations in multi-agent systems. Journal of Network Computing Applications 30(3), 1089–1115 (2007)CrossRefGoogle Scholar
  33. 33.
    Bateson, G.: Steps to an Ecology of Mind. University of Chicago Press, Chicago (1972)Google Scholar
  34. 34.
    Brooks, R.A.: A robot that walks; emergent behaviors from a carefully evolved network. Technical Report 1091, MIT Artificial Intelligence Laboratory (1989)Google Scholar
  35. 35.
    Steels, L., Kaplan, F.: Bootstrapping grounded word semantics. In: Briscoe, T. (ed.) Linguistic Evolution Through Language Acquisition: Formal and Computational Models, pp. 53–73. Cambdrige University Press, Cambridge (2002)CrossRefGoogle Scholar
  36. 36.
    Lin, T., Xie, Y., Wasilewska, A., Liau, C.J. (eds.): Data Mining: Foundations and Practice. SCI, vol. 118. Springer, New York (2008)Google Scholar
  37. 37.
    Bigus, J.P., Bigus, J.: Constructing Intelligent Agents Using Java. Professional Developer’s Guide Series. John Wiley & Sons, Inc., New York (2001)Google Scholar
  38. 38.
    Urlings, P.: Teaming Human and Machine: A conceptual framework for automation from an aeronautical perspective. PhD thesis, University of South Australia, School of Electrical and Information Engineering (2004)Google Scholar
  39. 39.
    Wooldridge, M., Jennings, N.R.: Theories, Architectures, and Languages: A Survey. In: Wooldridge, M.J., Jennings, N.R. (eds.) ECAI 1994 and ATAL 1994. LNCS (LNAI), vol. 890, pp. 1–39. Springer, Heidelberg (1995)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2013

Authors and Affiliations

  • Jeffrey W. Tweedale
    • 1
  • Ivan Jordanov
    • 2
  1. 1.School of Electrical and Information EngineeringUniversity of South AustraliaAdelaideAustralia
  2. 2.School of ComputingUniversity of PortsmouthHampshireUnited Kingdom

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