Skip to main content
SpringerLink
Log in

Wissensrepräsentation in der AI am Beispiel Semantischer Netze

  • Chapter
Artificial Intelligence — Eine Einführung

Part of the book series: Leitfäden der angewandten Informatik ((XLAI,volume 2))

Zusammenfassung

Aus den Erfahrungen der ersten Generation von Programmen, die „intelligente“ Leistungen erbringen sollten, ergab sich, daß die Bereitstellung geeigneten Umweltwissens (common sense knowledge) eine wesentliche Voraussetzung für den Erfolg von AI-Systemen ist. Ein Grund dafür ist, daß AI-Programme für Domänen entwickelt werden, in denen keine algorithmischen Problemlösungen bekannt sind, sondern in denen heuristische Methoden eingesetzt werden müssen. Effiziente Heuristiken beruhen aber meist darauf, daß dem System entsprechendes Wissen für seine Entscheidungen bereitsteht. Faktisch in allen Teilgebieten der AI sieht man sich daher mit dem Problem der Wissensrepräsentation (Knowledge Representation — KR) konfrontiert.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. Barr A., The Representation Hypothesis, Working Paper HPP-80–1, Stanford University 1980.

    Google Scholar 

  2. Bobrow D.G., A.Collins (eds.), Representation and Understanding: Studies in Cognitive Science, Academic Press, New York 1975.

    MATH  Google Scholar 

  3. Bobrow D.G., T.Winograd, An Overview of KRL, a Knowledge Representation Language, Cognitive Science, 1(77)3–46.

    Google Scholar 

  4. Brachman R.J., A Structural Paradigm for Representing Knowledge, BBN Report No.3605, Cambridge 1978.

    Google Scholar 

  5. / Brachman R., Cicarelli E., Greenfield N., The KLONE Reference Manual, BBN Report No.3848, Cambridge 1978.

    Google Scholar 

  6. Davis D.J., POPLER: A POP-2 Planner, Report No.MIP-89, School of Artificial Intelligence, University of Edinburgh, Edinburgh 1972.

    Google Scholar 

  7. Davis R., Buchanan B.G., Shortliffe E.H., Production Rules as a Representation for a Knowledge-Based Consultation System, Artificial Intelligence 8(77)15–45.

    Google Scholar 

  8. / Fahlman S.E., NETL: A System for Representing and Using Real-World Knowledge, MIT Press, Cambridge 1979.

    Google Scholar 

  9. Findler N.V.(ed.), Associative Networks, Academic Press, New York 1979.

    MATH  Google Scholar 

  10. Fillmore C., The Case for Case, In: E.Bach, R.Harms (eds.), Universals in Linguistic Theory, Holt, New York 1968.

    Google Scholar 

  11. Habel Ch., Schmidt A., Eine modallogische Repräsentations-sprache zur Darstellung von Wissen, In: W.Vandeweghe, M.Van de Velde (eds.), Bedeutung, Sprechakte und Texte, Niemeyer Verlag, Tübingen 1979.

    Google Scholar 

  12. Hayes Ph.J, Some Association Based Tecniques for Lexical Disambiguation by Machine, Thesis, Ecole Polytechnique Federale de Lausanne 1977.

    Google Scholar 

  13. Hays D.G., Types of Processes on Cognitive Networks, Proc. COLING-73.

    Google Scholar 

  14. Hendrix G.G., Encoding Knowledge in Partitioned Networks, In: /9/.

    Google Scholar 

  15. Hewitt C., Bishop P., Steiger R., A Universal Modular Actor Formalism for Artificial Intelligence, Proceedings IJCAI-73.

    Google Scholar 

  16. Kowalski R.A., Predicate Logic as a Programming Language, Proc.IFIP-74, Stockholm 1974.

    Google Scholar 

  17. Lehnert W.G., The Process of Question-Answering, Lawrence Erlbaum Ass., New Jersey 1978.

    MATH  Google Scholar 

  18. McDermott D., Sussman G., The CONNIVER Reference Manual, MIT AI Lab, Memo 269a, Cambridge 1974.

    Google Scholar 

  19. Minsky M., A Framework for Representing Knowledge, In: P.Winston (ed.), The Psychology of Computer Vision, McGraw-Hill, New York 1975.

    Google Scholar 

  20. Norman D.A., Rumelhart D.E, Strukturen des Wissens: Wege der Kognitionsforschung, Klett-Cotta, Stuttgart 1978.

    Google Scholar 

  21. Quillian R., Semantic Memory, In: M.Minsky (ed.), Semantic Information Processing, MIT Press, Cambridge 1968.

    Google Scholar 

  22. Raphael B., SIR: Semantic Information Retrieval, In: M.Minsky (ed.), Semantic Information Processing, MIT Press, Cambridge 1968.

    Google Scholar 

  23. Reboh R. et al., QLISP: A Language for the Interactive Development of Complex Systems, TN-120, SRI International, Menlo Park 1976.

    Google Scholar 

  24. Schank R.C., Conceptual Information Processing, North-Holland, Amsterdam 1975.

    MATH  Google Scholar 

  25. Schank R.C., Carbonell J.G., Re: The Gettysburg Address Representing Social and Political Acts, In: /9/.

    Google Scholar 

  26. Schubert L.K., The Structure and Organization of a Semantic Net for Comprehension and Inference, Dept.of CS, TR 78–1, Univ.of Alberta, Edmonton 1978.

    Google Scholar 

  27. Simmons R.F., Bruce B.C., Some Relations between Predicate Calculus and Semantic Net Representation of Discourse, Proc. IJCAI-71.

    Google Scholar 

  28. Wahlster W., Die Repräsentation von vagem Wissen in natürlichsprachigen Systemen der Künstlichen Intelligenz, IFI-HH-B-38, Institut für Informatik, Universität Hamburg, 1977.

    Google Scholar 

  29. Waterman D.A., Hayes-Roth F. (eds.), Pattern-Directed Inference Systems, Academic Press, New York 1978.

    MATH  Google Scholar 

  30. Winograd T., Frame Representations and The Declarative-Procedural Controversy, In: /2/, 1975.

    Google Scholar 

  31. Winograd T., Understanding Natural Language, Academic Press, New York London 1976.

    Google Scholar 

  32. Zilles N., Brodie M.(eds.), Proceedings of the Workshop on Data Abstraction, Databases and Conceptual Modelling, SIGART 74(1981).

    Google Scholar 

Download references

Authors
  1. Harald Trost
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 1986 B. G. Teubner Stuttgart

About this chapter

Cite this chapter

Trost, H. (1986). Wissensrepräsentation in der AI am Beispiel Semantischer Netze. In: Artificial Intelligence — Eine Einführung. Leitfäden der angewandten Informatik, vol 2. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-322-93997-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-322-93997-5_4

  • Publisher Name: Vieweg+Teubner Verlag, Wiesbaden

  • Print ISBN: 978-3-519-12473-3

  • Online ISBN: 978-3-322-93997-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation