On Creativity and Intelligence in Computational Systems

  • Stuart H. RubinEmail author
Part of the Intelligent Systems Reference Library book series (ISRL, volume 29)


This chapter presents an investigation of the potential for creative and intelligent computing in the domain of machine vision. It addresses such interrelated issues as randomization, dimensionality reduction, incompleteness, heuristics, as well as various representational paradigms. In particular, randomization is shown to underpin creativity, heuristics are shown to serve as the basis for intelligence, and incompleteness implies the need for heuristics in any non trivial machine vision application, among others. Furthermore, the evolution of machine vision is seen to imply the evolution of heuristics. This conclusion follows from the examples supplied herein.


Machine Vision Computational System Signature Vector Response Vector Auditory Image 
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.
    USN PEO for C4I and Space and USAF Electronic Systems Center. Net-Centric Enterprise Solutions for Interoperability, Net-Centric Implementation, Part 2: ASD (NII) Checklist Guidance 1.2 (20) (2005)Google Scholar
  2. 2.
    Rubin, S.H.: On the Auto-Randomization of Knowledge. In: Proc. IEEE Intern. Conf. Info. Reuse and Integration, Las Vegas, NV, pp. 308–313 (2004)Google Scholar
  3. 3.
    Lin, J.H., Vitter, J.S.: Complexity Results on Learning by Neural Nets. Mach. Learn. 6(3), 211–230 (1991)Google Scholar
  4. 4.
    Rubin, S.H.: On Randomization and Discovery. Info. Sciences 177(1), 170–191 (2007)CrossRefzbMATHGoogle Scholar
  5. 5.
    Rubin, S.H., Kountchev, R., Todorov, V., Kountcheva, R.: Contrast Enhancement with Histogram-Adaptive Image Segmentation. In: Proc. IEEE Intern. Conf. Info. Reuse and Integration, Waikaloa, HI, pp. 602–607 (2006)Google Scholar
  6. 6.
    Eccles, J.C.: Understanding of the Brain, 2nd edn. McGraw-Hill Co. (1976)Google Scholar
  7. 7.
    Rubin, S.H.: Computing with Words. IEEE Trans. Syst. Man, Cybern. 29(4), 518–524 (1999)Google Scholar
  8. 8.
    Zadeh, L.A.: From Computing with Numbers to Computing with Words – from Manipulation of Measurements to Manipulation of Perceptions. IEEE Trans. Ckt. Syst. 45, 105–119 (1999)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Pedrycz, W., Rubin, S.H.: Data Compactification and Computing with Words. Intern. J. Engineering Applications of Artificial Intelligence 23, 346–356 (2010)CrossRefGoogle Scholar
  10. 10.
    Uspenskii, V.A.: Gödel’s Incompleteness Theorem, Translated from Russian. Ves Mir Publishers (1987)Google Scholar
  11. 11.
    Mitchell, T.M.: Version Spaces: A Candidate Elimination Approach to Rule Learning, Ph.D. Thesis, Stanford University (1979)Google Scholar
  12. 12.
    Duncan, G.R.: Cheap Drones Could Replace Search-And-Rescue Choppers. New Scientist,
  13. 13.
    Ackerman, S.: Air Force Wants Drones to Sense Other Planes’ ‘Intent’,
  14. 14.
    Quinlan, J.R.: C4.5: Programs for machine learning (September 1997)Google Scholar
  15. 15.
    Quinlan, J.R.: Bagging, Boosting, and C4.5. In: Proc. of the Thirteenth National Conference on Artificial Intelligence, pp. 725–730. AAAI Press, MIT Press, Cambridge, MA (1996)Google Scholar
  16. 16.
    Kfoury, A.J., Moll, R.N., Arbib, M.A.: A Programming Approach to Computability. Springer, Heidelberg (1982)zbMATHGoogle Scholar
  17. 17.
    Rubin, S.H.: On Knowledge Amplification by Structured Expert Randomization (KASER), U.S. Patent No. 7,047,226. Space and Naval Warfare Systems Center, San Diego Biennial Review (2001)Google Scholar
  18. 18.
    Rubin, S.H.: CBFER: Case-Based Field-Effect Reasoning. USN Patent Pending, NC 100222 (2009)Google Scholar
  19. 19.
    Feigenbaum, E.A., Feldman, J. (eds.): Computers and Thought. McGraw-Hill Inc. (1963)Google Scholar
  20. 20.
    Chaitin, G.J.: Randomness and Mathematical Proof. Sci. Amer. 232(5), 47–52 (1975)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.SSC-Pacific, US NavySan DiegoUSA

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