Artificial Intelligence for Knowledge Management and Learning



In the previous chapter we identified what we understand as corporate knowledge and knowledge management for companies. We have discussed why it is advantageous and how knowledge management can make the competitive difference between two companies, specifically for service companies. We have highlighted the power of connectionist thinking for knowledge management and the example of our brain was used to introduce the concept of neural networks. In this chapter we will discuss two specific techniques which can be used while building connectionist networks: artificial neural networks and fuzzy logic. They are both considered as part of what we call artificial intelligence techniques. The combination of both promises to be able to generate some intelligence in decision making. In a first stage we will describe and elaborate the techniques. The next chapter gives examples of straight applications of connectionist networks (artificial neural networks) in real life corporate situations. There after we discuss some examples of real life cases in knowledge management or connectionist approaches to complex management cases.


Neural Network Fuzzy Logic Business Process Knowledge Management Fuzzy Rule 
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. Baets W, 1993a. IT for organizational change: beyond business process engineering. Business Change and Re-engineering. Vol. 1, Nr. 2, Autumn.Google Scholar
  2. Baets W, 1993b. Information Systems Strategic Alignment: A case in banking. Working Paper. Nijenrode University, The Netherlands.Google Scholar
  3. Baets W, 1995a. Artificiele neurale netwerken: het in kaart brengen van veranderingsprocessen en het meten van leren. Handboek effektief opleiden. 4.131Google Scholar
  4. Benjamin R and Levinson E, 1993. A Framework for Managing IT-enabled change. Sloan Management Review. Vol. 34, Nr. 4, Summer.Google Scholar
  5. Burr D, 1987. Experiments with a connectionist text reader. Proceedings of the First International Conference on Neural Networks. M Caudill and C Butler (eds.). Vol. 4, pp. 717–724. San Diego, CA: SOS Printing.Google Scholar
  6. Collins E, Ghosh S and Scofield C, 1988. An application of a multiple neural network learning system to evaluation of mortgage underwriting judgment. IEEE International Conference on Neural Networks. Vol. II: pp. 459–466.CrossRefGoogle Scholar
  7. Cottrell G, Munro P and Zipser D, 1987. Image Compression by Backpropagation: An example of extensional programming. Advances in Cognitive Science. Vol. 3. Norwood, NJ: Ablex.Google Scholar
  8. Cox, McNeill and Thro, Dubois and Prade, Zadeh, 1995. Fuzzy Logic CD-ROM Library. AP Professional.Google Scholar
  9. Dayhoff J, 1990. Neural Network Architectures. NY: Von Nostrand Reinhold Book.Google Scholar
  10. Dietz W, Kiech E, Ali M, 1989. Jet and Rocket engine fault diagnosis in real time. Journal Neural Network Computing. Vol.1, Nr. 1, pp. 5–18.Google Scholar
  11. Dreyfus H and Dreyfus S, 1988. “Making a Mind versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint.” In The artificial intelligence debate: false starts and real foundations, Gravbard S (ed.). MIT Press.Google Scholar
  12. Duda R and Hart P, 1973. Pattern classification and scene analysis. New York: Wiley Interscience.Google Scholar
  13. Dutta S, 1993. Knowledge Processing & Applied Artificial Intelligence. Butterworth-Heinemann.Google Scholar
  14. Hart A, 1992. Using Neural Networks for Classification Tasks - Some Experiments on Datasets and Practical advice. Journal of the Operational Research Society. Vol. 43, Nr. 3, March.Google Scholar
  15. Lin, C-T and Lee G, 1996. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice Hall.Google Scholar
  16. Marko K, James J, Dosdall J and Murphy J, 1989. Automotive control systems diagnostics using neural nets for rapid pattern classification of large data sets. Proceedings of International Joint Conference on Neural Networks. Vol. II: pp. 13–15.CrossRefGoogle Scholar
  17. Mc Neill D and Freibergn P, 1994. Fuzzy Logic. Touchstone. Simon & Schuster.Google Scholar
  18. Parker D, 1982. Learning-logic. Intervention Report. S81–64, File 1, Office of Technology Licensing, Stanford University.Google Scholar
  19. Pedrycz W, 1995. Fuzzy Sets Engineering. CRC Press.Google Scholar
  20. Rummelhart D and McClelland J, 1986. Parallel Distributed Processing: Exploration in the Microstructure of cognition. Vol. 1: Foundations. Cambridge, MA: MIT Press.Google Scholar
  21. Rummelhart D and McClelland J, 1986. Parallel Distributed Processing: Exploration in the Microstructure of cognition. Vol. 2: Psychological and Biological Models. Cambridge, MA: MIT Press.Google Scholar
  22. Sejnowski T and Rosenberg C, 1987. Parallel networks that learn to pronounce English text. Complex Systems. Vol. 3, pp. 145–168.Google Scholar
  23. van Wezel M and Baets W, 1995. Predicting Market Responses with a Neural Network: the Case of Fast Moving Consumer Goods. Marketing Intelligence & Planning. Autumn.Google Scholar
  24. Venugopal V and Baets W, 1994a. Neural Networks and Statistical Techniques in Marketing Research: A conceptual comparison. Marketing Intelligence & Planning. Vol. 12, Nr. 7.CrossRefGoogle Scholar
  25. Venugopal V and Baets W, 1994b. Neural Networks and their Applications in Marketing Management. The Journal of Systems Management. September.Google Scholar
  26. Wasserman P, 1989. Neural Computing: Theory and practice. NY: Von Nostrand Reinhold Book.Google Scholar
  27. Werbos P, 1974. Beyond Regression: New tools for prediction and analysis in the behavioral sciences. Masters thesis. Harvard University.Google Scholar
  28. Widrow B and Stearns S, 1985. Adaptive signal processing. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  29. Yoon Y, Brobst R, Bergstresser P and Petersen, 1989. A Desktop Neural Network for dermatology diagnosis. Journal on Neural Network Computing. Vol.l, Nr. 1, pp. 43–52.Google Scholar

Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  1. 1.The Netherlands Business SchoolNijenrode UniversityThe Netherlands

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