Skip to main content

Artificial intelligence methoden im CUU

  • Conference paper
  • First Online:
Rechner-Gestützter Unterricht (RGU 1974)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 17))

  • 1961 Accesses

Abstract

The applicability of current research methods in artificial intelligence to computer-aided instruction is considered. The most promising contributions to the design of systems able to carry on human-like dialogue should be expected from semantic networks and theorem-proving techniques. General problem-solving methods — such as the state-space method or the problem-reduction method — can be applied to problem generation, selection, and solving.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. Bobrow, D.G. & B. Raphael: New Programming Languages for AI Research. Lecture presented at Third International Joint Conference on Artificial Intelligence, Stanford, Calif., 1973.

    Google Scholar 

  2. Carbonell, J.R.: Mixed-initiative Man-Computer Instructional Dialogues. BBN Report No. 1971, Bolt Beranek and Newman Inc., Cambridge, Mass., 1971.

    Google Scholar 

  3. Charniak, E. Toward a model of children's story comprehension. AI-TR-266, M.I.T., Artificial Intelligence Laboratory, Cambridge,Mass., 1972.

    Google Scholar 

  4. Coles, L.S.: Computer-aided Instruction Using an Inferential Question Answering System with Natural Language Input: A Plan for Research. Techn. Note 11, Stanford Research Institute, Artificial Intelligence Group, 1969.

    Google Scholar 

  5. Dear, R.E. & Atkinson, R.C.: Optimal Allocation of items in a simple Two-concept Automated Teaching Model. In J.E. Coulson (Ed.) Programmed Learning and Computer-Based Instruction.N.Y.:Wiley, 1962, 25–45.

    Google Scholar 

  6. Gagné, R.M. The Conditions of Learning,New York, 1965.

    Google Scholar 

  7. Gilkey, T.J.&E.B. Koffman: Generative CAI in High-School Algebra. In A.Günther et al. (Eds.) Intern. Comp. Symp. 1973.Amsterdam, 1974, 489–494.

    Google Scholar 

  8. Goldberg, A.: A Generalized Instructional System for Elementary Mathematical Logic. T.R.179,IMSSS,Stanford University, Stanford, 1971.

    Google Scholar 

  9. Hewitt, C.: PLANNER: A Language for Proving Theorems in Robots. In D.E. Walker & L.M. Norton (Eds.) Proceedings of the Intern.Joint Conf. on Artif. Intell., Boston: MITRE Corp., 1969.

    Google Scholar 

  10. Koffman, E.B.&S. Blount: Artificial Intelligence and Automatic Programming in CAI. In Proc. Third Intern. Conf. on Artif. Intell.,Stanford Research Institute, 1973, 86–94.

    Google Scholar 

  11. Koffman,E.B., S.Blount & M.Wei: CAI in Digital Logic Design, Debugging and Programming. Comput.&Elect.Engin. 1,299–320.

    Google Scholar 

  12. Laubsch, J.H.&A.Chiang: Application of Mathematical Models of Learning in the Decision Structure of Adaptive CAI-systems.[7],481–487.

    Google Scholar 

  13. Newell,A.&H.Simon: Human Problem Solving.Englewood Cliffs,1972.

    Google Scholar 

  14. Nilsson, N.: Problem-Solving Methods in Artificial Intelligence. New York:McGraw-Hill,1971.

    Google Scholar 

  15. Quillian, M.R.: Semantic Memory. In M.L. Minsky (Ed.) Semantic Information Processing. Cambridge, Mass.:M.I.T.Press,1968.

    Google Scholar 

  16. Robinson, J.A. A Machine-Oriented Logic Based on the Resolution Principle. J.ACM,12,1965,23–41.

    Article  Google Scholar 

  17. Rumelhart, D.E.,P.H. Lindsay & D.A. Norman: A Process Model for Long Term Memory.In Tulving & Donaldson (Eds.) Organisation and Memory. New York:Academic Press,1972.

    Google Scholar 

  18. Sandewall, E.J. Representing Natural Language Information in Predicate Calculus. In B. Meltzer & D. Michie (Eds.) Machine Intelligence 6. Edinburgh, U.K.:Edinburgh University Press,1969.

    Google Scholar 

  19. Schank, R.:Conceptual Dependency: A Theory of Natural Language Understanding. Cognitive Psychology,3,Nr.4,1972.

    Google Scholar 

  20. Simmons, R.F.: Semantic Networks: Their Computation and Use for Understanding English Sentences. In R.Schank & K.Colby (Eds.) Computer Models of Thought and Language. San Francisco,1973.

    Google Scholar 

  21. Simmons,R.F. & B.Bruce: Some Relations between Predicate Calculus and Semantic Net Representations of Discourse. Proc. of the 2nd Int. Joint Conf. on Artif. Intell.,London:Brit.Comp.Soc.,1971.

    Google Scholar 

  22. Simmons,R.F. & J.Slocum: Generating English Discourse from Semantic Nets. Comm.ACM,15,1972.

    Google Scholar 

  23. Sussman,G., T.Winograd & E.Charniak: Micro Planner Reference Manual. A.I.Memo 203A, Artif. Intell. Lab.,M.I.T.,1970.

    Google Scholar 

  24. Van Campen, J. Towards the automatic generation of programmed foreign-language instructional materials,T.R.163,IMSSS, Stanford,1971.

    Google Scholar 

  25. Winograd, T. Understanding Natural Language.New York,1972.

    Google Scholar 

  26. Woods, W.A.: Transition Network Grammars for Natural Language Analysis. Comm.ACM, 13,1970,591–606.

    Article  Google Scholar 

Download references

Authors

Editor information

K. Brunnstein K. Haefner W. Händler

Rights and permissions

Reprints and permissions

Copyright information

© 1974 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Laubsch, J.H. (1974). Artificial intelligence methoden im CUU. In: Brunnstein, K., Haefner, K., Händler, W. (eds) Rechner-Gestützter Unterricht. RGU 1974. Lecture Notes in Computer Science, vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-06907-0_98

Download citation

  • DOI: https://doi.org/10.1007/3-540-06907-0_98

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-06907-2

  • Online ISBN: 978-3-540-37847-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics