Advertisement

Algorithm animation with Agat

  • Olivier Arsac
  • Stéphane Dalmas
  • Marc Gaëtano
Part of the Texts and Monographs in Symbolic Computation book series (TEXTSMONOGR)

Abstract

Algorithm animation is a powerful tool for exploring a program’s behavior. It is used in various areas of computer science, such as teaching (Rasala et al. 1994), design and analysis of algorithms (Bentley and Kernighan 1991), performance tuning (Duisberg 1986). Algorithm animation systems provide a form of program visualization that deals with dynamic graphical displays of a program’s operations. They offer many facilities for users to view and interact with an animated display of an algorithm, by providing ways to control through multiple views the data given to algorithms and their execution.

Keywords

Design Principle Common Denominator Computer Algebra System Homework Problem Correctness Principle 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Avitzur, R., Bachmann, O., Kajler, N. (1995): From honest to intelligent plotting. In: Levelt, A. H. M. (ed.): Proceedings of the ACM International Symposium on Symbolic and Algebraic Computation (ISSAC’ 95), Montreal, Canada. Association for Computing Machinery, New York, pp. 32–41.Google Scholar
  2. Beeson, M. (1989): Logic and computation in Mathpert: an expert system for learning mathematics. In: Kaltofen, E., Watt, S. M. (eds.): Computers and mathematics. Springer, Berlin Heidelberg New York Tokyo, pp. 202–214.CrossRefGoogle Scholar
  3. Beeson, M. (1990): Mathpert: a computerized environment for learning algebra, trig. and calculus. J. Artif. Intell. Educ. 2: 1–11.MathSciNetCrossRefGoogle Scholar
  4. Beeson, M. (1992): Mathpert: computer support for learning algebra, trigonometry, and calculus. In: Voronkov, A. (ed.): Logic programming and automated reasoning. Springer, Berlin Heidelberg New York Tokyo, pp. 453–456 (Lecture notes in computer science, vol. 624).Google Scholar
  5. Beeson, M. (1995): Using nonstandard analysis to verify the correctness of computations. Int. J. Found. Comput. Sci. 6: 299–338.MATHCrossRefGoogle Scholar
  6. Brockschmidt, K. (1994): Inside OLE 2. Microsoft Press, Redmond, WA.Google Scholar
  7. Buchberger, B. (1990): Should students learn integration rules? ACM SIGSAM Bull. 24: 10–17.CrossRefGoogle Scholar
  8. Bundy, A. (1983): The computer modelling of mathematical reasoning. Academic Press, London.MATHGoogle Scholar
  9. Burton, R. R. (1982): Diagnosing bugs in a simple procedural skill. In: Sleeman, D. H., Brown, J. S. (ed.): Intelligent tutoring systems. Academic Press, London, pp. 157–185.Google Scholar
  10. Clarke, E., Zhao, X. (1992): Analytica: an experiment in combining theorem proving and symbolic manipulation. Technical Report CMU-CS-92-1 7, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA.Google Scholar
  11. Fateman, R. (1992): Honest plotting, global extrema, and interval arithmetic. In: Wang, P. S. (ed.): Proceedings of the ACM International Symposium on Symbolic and Algebraic Computation (ISSAC’ 92), Berkeley, California. Association for Computing Machinery, New York, pp. 216–223.Google Scholar
  12. Nguyen-Xuan, F., Nicaud, J. F., Gelis, J. M., Joly, F. (1993): Automatic diagnosis of the student’s knowledge state in the learning of algebraic problem solving. In Brna, P., Ohlsson, S., Pain, H. (eds.): Artificial intelligence in education. Association for the Advancement of Computing in Education, Charlottesville, VA, pp. 489–496.Google Scholar
  13. Nicaud, J. F. (1994): Building ITSs to be used: lessons learned from the APLUSIX project. In: Lewis, R., Mendelsohn, P. (eds.): Lessons from learning. North-Holland, Amsterdam, pp. 181–198 (IFIP transactions, series A, vol. 46).Google Scholar
  14. Nicaud, J. F., Gelis, J. M., Saidi, M. (1993): A framework for learning polynomial factoring with new technologies. In: International Conference on Computers in Education 93, Taiwan, pp. 288–293.Google Scholar
  15. Richardson, D. (1968): Some unsolvable problems involving elementary functions of a real variable. J. Symb. Logic 33: 511–520.Google Scholar
  16. Wu, W.-T (1986): Basic principles of mechanical theorem-proving in elementary geometries. J. Automat. Reason. 2: 221–252.MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • Olivier Arsac
  • Stéphane Dalmas
  • Marc Gaëtano

There are no affiliations available

Personalised recommendations