Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bloom, F.E. and Lazerson, A. (1999), Brain, Mind, and Behaviour (2nd edition),W.H. Freeman and Company, Worth Publishers.
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.
Eberhart R., Simpson, P., and Dobbins R. (1996), Computation Intelligence PC Tools, Academic Press, New York.
Goddard, N. and Hood, N. (1997), “Parallel GENESIS for large scale modeling,” in Bower, J.M. (ed.), Computational Neuroscience ‘86, Plenum, New York.
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.
Hasebrook, J. (1995), “Lernen mit Multimedia (Learning with multimedia),” German Journal of Educational Psychology, vol. 9, no. 2, pp. 95–103.
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.
Hasebrook, J.P. (1998), “Searching the Web without losing the mind,” Webnet Journal, vol. 1, no. 2, pp. 24–32
Haykin, S. (1994), Neural Networks, a Comprehensive Foundation, New York: Macmillan College Publishing Company.
Kosko, B. (1992), Neural Networks for Signal Processing, Prentice-Hall, Englewood Cliffs, NJ.
Maass, W. (1997), “Networks of Spiking Neurons: the Third Generation of Neural Network Models,” Graz: Institute for Theoretical Computer Science, TU Graz.
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.
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.
Moravec, H. (1998), “When will computer hardware match the human brain?” Journal of Transhumanism,vol. 1, http://www.transhumanist.com/volume1/moravec.htm.
Sheperd, G.M. (ed.) (1998), The Synaptic Organization of the Brain, Oxford University Press, Oxford.
Sproull, L. and Kiesler, S. (1991), Connections: New Ways of Working in the Networked Organization, MIT Press, Cambridge MA.
Turing, A.M. (1950), “Computing machinery and intelligence,” Mind, vol. 59, pp. 433–460.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hasebrook, J., Erasmus, L., Doeben-Henisch, G. (2002). Knowledge Robots for Knowledge Workers: Self-Learning Agents Connecting Information and Skills. In: Jain, L.C., Chen, Z., Ichalkaranje, N. (eds) Intelligent Agents and Their Applications. Studies in Fuzziness and Soft Computing, vol 98. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1786-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1786-7_3
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2510-7
Online ISBN: 978-3-7908-1786-7
eBook Packages: Springer Book Archive