Representing semantic knowledge with 2-dimensional rules in the domain of functional programming

  • Claus Möbus
  • Olaf Schröder
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 439)


One of the many difficult problems in the development of intelligent computer aided instruction (ICAI) is the appropriate design of instructions and helps. This paper addresses the question of optimizing instructional and help material concerning the operational knowledge for the visual, functional programming language ABSYNT (ABstract SYNtax Trees). The ultimate goal of the project is to build a problem solving monitor (PSM) for this language and the corresponding programming environment. The PSM should analyse the blueprints of the students, give comments and proposals (SLEEMAN & HENDLEY, 1982). First, we will explain our motivation for choosing this domain of discourse. Second, we will shortly present the programming environment of ABSYNT. Third, we represent the development of two alternative 2-D-rulesets (appendix A, B), which describe the operational semantics of the ABSYNT interpreter. The development of the 2-D-rules was guided by cognitive psychology and cognitive engineering aspects and results of an empirical study. The study showed that the rules were comprehensible even for computer novices.


Work Memory Load Horn Clause Start Tree Computational Step Input Field 
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. ALBER, K., STRUCKMANN, W., Einführung in die Semantik von Programmiersprachen, Mannheim: BI-Wissenschaftsverlag, 1988Google Scholar
  2. BAUER, F.L., GOOS, G.: Informatik, 1.Teil. Berlin, Springer, 1982 (3. Edition)Google Scholar
  3. BOURNE, L.E.: An Inference Model of Conceptual Rule Learning. In: SOLSO, R. (ed): Theories in Cognitive Psychology. WASHINGTON, D.C.: ERLBAUM, 1974, 231–256Google Scholar
  4. BROWN, J.S.; van LEHN, K.: Repair Theory: A Generative Theory of Bugs in Procedural Skills. Cognitive Science, 1980, 4, 379–426Google Scholar
  5. CARROLL, J.M.: Minimalist Design for Active Users. In: SHACKLE, B. (ed): Interact 84, First IFIP Conference on Human-Computer-Interaction. Amsterdam: Elsevier/North Holland, 1984aGoogle Scholar
  6. CARROLL, J.M.: Minimalist Training. Datamation, 1984b, 125–136Google Scholar
  7. CHANG, S.K., Visual Languages: A Tutorial and Survey, in: P. GORNY & M.J. TAUBER (eds), Visualization in Programming, Lecture Notes in Computer Science, Heidelberg: Springer, 1987, 1–23Google Scholar
  8. CHASE, W. G., Visual Information Processing, in: K.R. BOFF, L. KAUFMAN, J.P. THOMAS (eds), Handbook of Perception and Human Performance, Vol. II, Cognitive Processes and Performance, New York: Wiley, 1986, 28–1–28–71Google Scholar
  9. COLONIUS, H., FRANK, K.D., JANKE, G., KOHNERT, K., MÖBUS, C., SCHRÖDER, O., THOLE, H.J., Stand des DFG-Projekts "Entwicklung einer Wissensdiagnostik-und Fehlererklärungskomponente beim Erwerb von Programmierwissen für ABSYNT", in: R. GUNZENHÄUSER, H. MANDL (eds), "Intelligente Lernsysteme", Institut für Informatik der Universität Stuttgart, Deutsches Institut für Fernstudien an der Universität Tübingen, 1987, 80–90Google Scholar
  10. COLONIUS, H., FRANK, K.D., JANKE, G., KOHNERT, K., MÖBUS, C., SCHRÖDER, O., THOLE, H.J., Syntaktische und semantische Fehler in funktionalen graphischen Programmen, ABSYNT Report 2/87, 1987Google Scholar
  11. DAVIS, R.E., Runnable Specification as a Design Tool, in: K.L. CLARK, S.A. TÄRNLUND (eds), Logic Programming, New York: Academic Press, 1982, 141–149Google Scholar
  12. FITTER, M; GREEN, T.R.G.: When Do Diagrams Make Good Computer Languages? Int. Journal of Man-Machine Studies, 1979, 11, 235–261, and in: COOMBS, M.J.; ALTY, J.L. (eds): Computing Skills and the User Interface. New York: Academic Press, 1981, 253–287Google Scholar
  13. GENESERETH, M.R.; NILSSON, N.J.: Logical Foundations of Artificial Intelligence. Los Altos, California: Morgan Kaufman, 1987Google Scholar
  14. GREEN, T.R.G.; SIME, M.E.; FITTER, M.J.: The Art of Notation. In: COOMBS, M.J.; ALTY, J.L. (eds): Computing Skills and the User Interface. New York: Academic Press, 1981, 221–251Google Scholar
  15. HAYGOOD, R.C.; BOURNE, L.E.; Attribute-and Rule Learning Aspects of Conceptual Behaviour. Psychological Review, 1965, 72, 175–195Google Scholar
  16. JANKE, G., KOHNERT, K., Interface Design of a Visual Programming Language: Evaluating Runnable Specifications, in: F. KLIX, H. WANDKE, N.A. STREITZ, Y. WAERN (eds), Man-Computer Interaction Research, MACINTER II, Amsterdam: North-Holland, 1989, 567–581Google Scholar
  17. JOHNSON, W.L.; SOLOWAY, E: PROUST: An Automatic Debugger for PASCAL Programs. BYTE, 1985, April, 179–190, and in KEARSLEY, G.P. (ed): Artificial Intelligence and Instruction. Reading, Mass.: Addison Wesley, 1987, 49–67Google Scholar
  18. KOHNERT, K., JANKE, G.: The Object-Oriented Inplementation of the ABSYNT-Environments. ABSYNT-Report 4/88, Project ABSYNT, FB 10, Unit on Tutoring and Learning Systems, University of Oldenburg, 1988Google Scholar
  19. LARKIN, J.H.; SIMON, H.A.: Why a Diagram is (Sometimes) Worth More Than Ten Thousand Words. Cognitive Science, 1987, 11, 65–99Google Scholar
  20. MEDIN, D.L.; WATTENMAKER, W.D.; MICHALSKI, R.S.: Constraints and Preferences in Inductive Learning: An Experimental Study of Human and Machine Performance. Cognitive Science, 1987, 11, 299–339Google Scholar
  21. MIYAKE, N.: Constructive Interaction and the Iterative Process of Understanding. Cognitive Science, 10, 1986, 151–177Google Scholar
  22. MÖBUS, C., Die Entwicklung zum Programmierexperten durch das Problemlösen mit Automaten, in: MANDL, FISCHER (Hrsgb), Lernen im Dialog mit dem Computer, München: Urban & Schwarzenberg, 1985, 140–154Google Scholar
  23. MÖBUS, C., SCHRÖDER, O., Knowledge Specification and Instructions for a Visual Computer Language, in: F. KLIX, H. WANDKE, N.A. STREITZ, Y. WAERN (eds), Man-Computer Interaction Research, MACINTER II, Amsterdam: North-Holland, 1989, 535–565Google Scholar
  24. MÖBUS, C., THOLE, H.J., Tutors, Instructions and Helps, in: CHRISTALLER, Th. (ed), Künstliche Intelligenz. KIFS87, Proceedings, Informatik-Fachberichte 202, Heidelberg: Springer, 1989, 336–385Google Scholar
  25. PAGAN, F.G., Formal Specification of Programming Languages, Englewood Cliffs, N.J.: Prentice-Hall, 1981Google Scholar
  26. PAYNE, S.J.; SIME, M.E.; GREEN, T.R.G.: Perceptual Structure Cueing in a Simple Command Language. Int. Journal of Man-Machine Studies, 1984, 21, 19–29Google Scholar
  27. PENNINGTON, N.: Stimulus Structures and Mental Representations in Expert Comprehension of Computer Programs. Cognitive Psychology, 1987, 19, 295–341Google Scholar
  28. POMERANTZ, J.R.: Perceptual Organization in Information Processing. In: AITKENHEAD, A.M.; SLACK, J.M. (eds): Issues in Cognitive Modeling. Hillsdale: Erlbaum, 1985, 127–158Google Scholar
  29. SCHRÖDER, O., FRANK, K.D., COLONIUS, H., Gedächtnisrepräsentation funktionaler, graphischer Programme, ABSYNT-Report 1/87, Universität Oldenburg, 1987Google Scholar
  30. SHU, N.C., Visual Programming Languages: A Perspective and a Dimensional Analysis, in: CHANG, T., ICHIKAWA, LIGOMENIDES, P.A. (eds), Visual Languages, New York: Plenum Press, 1986, 11–34Google Scholar
  31. SLEEMAN, D.H., HENDLEY, R.J., ACE: A system which Analyses Complex Explanations, in: D. SLEEMAN, J.S. BROWN (eds), Intelligent Tutoring Systems, New York: Academic Press, 1982, 99–118Google Scholar
  32. SOLOWAY, E.: Learning to Program = Learning to Construct Mechanisms and Explanations. Communications of the ACM, 29, 9, 1986, 850–858Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Claus Möbus
    • 1
  • Olaf Schröder
    • 1
  1. 1.Project Absynt FB 10, Informatik Unit on Tutoring and Learning SystemsUniversity of OldenburgOldenburgFRG

Personalised recommendations