A framework for ICAI systems based on inductive inference and logic programming

  • Kazuhisa Kawai
  • Riichiro Mizoguchi
  • Osamu Kakusho
  • Jun'ichi Toyoda
Session 2b: Inductive Inference And Debugging
Part of the Lecture Notes in Computer Science book series (LNCS, volume 225)


The main components of an Intelligent Computer-Assisted Instruction (ICAI) system are the expertise, the student model and tutoring strategies. The student model manages what the student does and does not understand, and the performance of an ICAI system depends largely on how well the student model approximates the human student. We propose a new framework for ICAI systems which uses the inductive inference for constructing the student model from the student's behavior. In the framework, both the expertise and the student model are represented as Prolog programs, which enables to express the meta-knowledge that is the knowledge of how to use the knowledge. Since the construction of the student model is performed independently of the expertise, the framework is domain-independent. Therefore, an ICAI system for any subject area can be built with the framework. As an example, the ICAI system teaching chemical reaction is presented together with a sample performance. The authors believe that the new framework for ICAI systems based on logic programming and inductive inference could be a breakthrough of the future ICAI systems.


Logic Programming Inductive Inference Volatile Acid Student Model Prolog Program 
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. [1]
    Carbonell, J.R.: AI in CAI: An artificial intelligence approach to computer-aided instruction, IEEE Trans. Man-Mach. Syst., Vol.MMS-11, No.4, pp.190–202 (1970).Google Scholar
  2. [2]
    Barr, A. and Feigenbaum, E.A.: The Handbook of Artificial Intelligence, Vol.II, PITMAN, London, pp.225–235 (1983).Google Scholar
  3. [3]
    Clocksin, W.F. and Mellish, C.S.: Programming in Prolog, Springer-Verlag, New York (1981).Google Scholar
  4. [4]
    Angluin, D. and Smith, C.H.: Inductive Inference: Theory and Methods, ACM Comput. Surv., Vol.15, No.3, pp.237–269 (1983).Google Scholar
  5. [5]
    Clancey, W.J.: Tutoring rules for guiding a case method dialogue, in Sleeman, D. et al. (ed.), Intelligent Tutoring Systems, Academic Press, London, pp.201–225 (1982).Google Scholar
  6. [6]
    Brown, J.S. and Burton, R.R.: Diagnostic models for procedural bugs in basic mathematical skills, Cognitive Science 2, pp.155–192 (1978).Google Scholar
  7. [7]
    Shapiro, E.Y.: Algorithmic Program Debugging, MIT Press, London (1982).Google Scholar
  8. [8]
    Sleeman, D. and Hendley, R.J.: ACE: A system which Analyses Complex Explanations, in Sleeman, D. et al. (ed.), Intelligent Tutoring Systems, Academic Press, London, pp.99–118 (1982).Google Scholar
  9. [9]
    Ganke,M. et al.: An ICAI System for Prolog Programming Based on Inductive Inference, Proc. of 28th National Conference of Information Processing Society of Japan, 1G-1, Tokyo, [in Japanese] (1984).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1986

Authors and Affiliations

  • Kazuhisa Kawai
    • 1
  • Riichiro Mizoguchi
    • 2
  • Osamu Kakusho
    • 2
  • Jun'ichi Toyoda
    • 2
  1. 1.Department of Information and Computer ScienceToyohashi University of TechnologyToyohashi, AichiJapan
  2. 2.The Institute of Scientific and Industrial ResearchOsaka UniversityOsakaJapan

Personalised recommendations