Summary
In recent years concepts and techniques have been developed in Computer Science and, in particular, in Artificial Intelligence which open up possibilities of a special kind. It is now possible to apply sophisticated programs to tasks which would require intelligence if carried out by humans, for example intelligent decision making, medical consultation or automatic image interpretation. One of the key reasons for this progress is the development of knowledge representation techniques and knowledge-based system architectures. It is shown by means of introductory examples that a knowledge-based approach offers several advantages, including transparency, adaptability, and an improved user interface. The main part of the presentation will deal with different ways of representing knwoledge. It is shown how IF-THEN rules may be used to represent knowledge in so-called expert systems which are designed to outperform human experts in certain domains, e.g. in medical diagnosis. While the resulting system architecture is particularly simple, such rules are certainly not adequate for representing highly structured knowledge. Several other techniques are described, including semantic nets which expose the interrelationships between pieces of knowledge by named links and lend themselves to an illustrative graphical representation. As the need arises to represent more aspects of this world in more detail and more depth, knowledge representation mechanisms have to fullfill sophisticated and mainfold requirements. The presentation concludes with some advanced methods and forthcoming applications.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Literaturhinweise
Buchanan and Shortliffe 84 Rule-Based Expert Systems B. G. Buchanan, E. H. Shortliffe Addison-Wesley, 1964
Charniak and McOermott 85 Introduction to Artificial Intelligence E. Charniak, D. McDermott McGraw-Hill, 1985
Ernst and Newell 69 GPS: A Case Study in Generality and Problem Solving G. W. Ernst, A. Newell Academic Press, 1969
Hendrix 79 Encoding Knowledge in Partitioned Networks G. G. Hendrix in: N. V. Findler (ed.), Associative Networks, Academic Press, 1979, 51-92
Quillian 68 Semantic Memory M. R. Quillian in: M. L. Minsky (ed.), Semantic Information Processing, The MIT Press, 1968. 216-270
Roberts and Goldstein 77 The FRL Primer R. B. Roberts, I. P. Goldstein Report AIM-408, MIT AI-Lab, Cambridge, MA, 1977
Schubert et al. 83 Determining Type, Part, Color, and Time Relationships L. K. Schubert, M. A. Papalaskaris, J. Taugher IEEE-Computer. October 1983, 53-60
Waterman and Hayes-Roth 78 Pattern-Directed Inference Systems O. A. Waterman, F. Hayes-Roth Academic Press 78
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1985 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Neumann, B. (1985). Wissensrepräsentation für Fortgeschrittene Computer-Anwendungen. In: Jesdinsky, H.J., Trampisch, H.J. (eds) Prognose- und Entscheidungsfindung in der Medizin. Medizinische Informatik und Statistik, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-82651-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-82651-1_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-16068-7
Online ISBN: 978-3-642-82651-1
eBook Packages: Springer Book Archive