Knowledge Robots for Knowledge Workers: Self-Learning Agents Connecting Information and Skills

  • J. Hasebrook
  • L. Erasmus
  • G. Doeben-Henisch
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 98)


The use of a desktop computer is restricted in many ways for the ordinary person today. People are not able to cope with the exponential growth of information and the increasing speed of information growth and business processes made possible by information and communication technologies. People have lost control over the information universe or infoverse. Intelligent technical support, not only for information storage and retrieval, but also for information selection, process planning, and decision support is needed. It is predicted that smart and mobile computing units embedded in a variety of appliances, such as TV sets and cars, will bring computing power and the common users of these intelligent appliances closer to each other by using natural language and social skills together with computer mediated communication. A general architecture of a knowledge robot or knowbot is described, based on a multi-agent platform and distributed computational intelligence. Knowbots consist of self-learning artificial brains connected to input sensors and output actuators of which speech recognition and synthesis are used to connect to networks of people. They have access to other software agents and computer programs through direct access or a multi-agent platform. A newly defined partnership between people and machines equipped with knowbots are a way to keep in control of the exploding infoverse.


Speech Recognition Intelligence Quotient Software Agent Knowledge Worker Computer Mediate Communication 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bloom, F.E. and Lazerson, A. (1999), Brain, Mind, and Behaviour (2nd edition),W.H. Freeman and Company, Worth Publishers.Google Scholar
  2. Doeben-Henisch, G. (1998), “Semiotic machines — an introduction,” in Luettich, E.W.B., Mueller, J.E., and Van Zoest, A. (eds.), Signs and Space, Raum and Zeichen. An international conference on the semiotics of space and culture, Gunter Narr, Tübingen, pp. 313327.Google Scholar
  3. Eberhart R., Simpson, P., and Dobbins R. (1996), Computation Intelligence PC Tools, Academic Press, New York.Google Scholar
  4. Goddard, N. and Hood, N. (1997), “Parallel GENESIS for large scale modeling,” in Bower, J.M. (ed.), Computational Neuroscience ‘86, Plenum, New York.Google Scholar
  5. Harasim, L.M., Hiltz, S.R., Teles, L., and Turoff, M. (eds.) (1995), Learning Networks: a Field Guide to Teaching and Learning Online, MIT Press, Boston.Google Scholar
  6. Hasebrook, J. (1995), “Lernen mit Multimedia (Learning with multimedia),” German Journal of Educational Psychology, vol. 9, no. 2, pp. 95–103.Google Scholar
  7. Hasebrook, J. and Nathusius, W. (1997), “An expert advisor for vocational guidance,” Journal of Artificial Intelligence in Education, vol. 8, no. 1, pp. 21–41.Google Scholar
  8. Hasebrook, J.P. (1998), “Searching the Web without losing the mind,” Webnet Journal, vol. 1, no. 2, pp. 24–32Google Scholar
  9. Haykin, S. (1994), Neural Networks, a Comprehensive Foundation, New York: Macmillan College Publishing Company.MATHGoogle Scholar
  10. Kosko, B. (1992), Neural Networks for Signal Processing, Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
  11. Maass, W. (1997), “Networks of Spiking Neurons: the Third Generation of Neural Network Models,” Graz: Institute for Theoretical Computer Science, TU Graz.Google Scholar
  12. McDermott, D. and Poggio, T. (1989), “Artificial intelligence,” in Hoperoft, J.E. and Kennedy, K.W. (chairs), Computer Science Achievements and Opportunities, Report of the NSF Advisory Committee for Computer Research, Society for Industrial and Applied Mathematics, Philadelphia, pp. 41–50.Google Scholar
  13. Menzel, R. (1996), “Neuronale Plastizität, Lernen und Gedächtnis,” in Dudel, J., Menzel, R., and Schmidt, R.F. (eds.), Neurowissenschaft. Vom Molekül zur Kognition, Springer, New York, pp. 485518.Google Scholar
  14. Moravec, H. (1998), “When will computer hardware match the human brain?” Journal of Transhumanism,vol. 1,
  15. Sheperd, G.M. (ed.) (1998), The Synaptic Organization of the Brain, Oxford University Press, Oxford.Google Scholar
  16. Sproull, L. and Kiesler, S. (1991), Connections: New Ways of Working in the Networked Organization, MIT Press, Cambridge MA.Google Scholar
  17. Turing, A.M. (1950), “Computing machinery and intelligence,” Mind, vol. 59, pp. 433–460.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • J. Hasebrook
  • L. Erasmus
  • G. Doeben-Henisch

There are no affiliations available

Personalised recommendations