Minds and Machines

, Volume 2, Issue 3, pp 267–282 | Cite as

Problem representation for refinement

  • H. Altay Guvenir
  • Varol Akman
General Articles


In this paper we attempt to develop a problem representation technique which enables the decomposition of a problem into subproblems such that their solution in sequence constitutes a strategy for solving the problem. An important issue here is that the subproblems generated should be easier than the main problem. We propose to represent a set of problem states by a statement which is true for all the members of the set. A statement itself is just a set of atomic statements which are binary predicates on state variables. Then, the statement representing the set of goal states can be partitioned into its subsets each of which becomes a subgoal of the resulting strategy. The techniques involved in partitioning a goal into its subgoals are presented with examples.

Key words

Problem-solving strategy problem representation refinement machine learning mechanical discovery 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Amarel, S. (1968), ‘On Representation of Problems of Reasoning about Actions’, in D. Michie, ed.,Machine Intelligence 3, Edinburgh, Scotland: Edinburgh University Press.Google Scholar
  2. Banerji, R. B. (1980),Artificial Intelligence: a Theoretical Approach New York, NY: North-Holland.Google Scholar
  3. Ernst, G. W. and Goldstein, M. M. (1982), ‘Mechanical Discovery of Classes of Problem-Solving Strategies’,Journal of ACM 29, pp 1–23.MATHMathSciNetCrossRefGoogle Scholar
  4. Ernst, G. W. and Newell, A. (1969),GPS: A Case Study in Generality and Problem Solving, New York, N.Y.: Academic Press.Google Scholar
  5. Guvenir, H. A. and Ernst, G. W. (1990), ‘Learning Problem Solving Strategies Using Refinement and Marco Generation’,Artificial Intelligence 44, pp. 209–243.CrossRefGoogle Scholar
  6. Korf, R. E. (1985),Learning to Solve Problems by Searching for Macro-Operators, Boston, MA: Pitman Advanced Publishing Program.Google Scholar
  7. Lauriere, J.-L. (1990),Problem Solving and Artificial Intelligence, London: Prentice-Hall.Google Scholar
  8. Newell, A. and Simon, H. A. (1972),Human Problem Solving, Englewood Cliffs, N.J.: Prentice Hall.Google Scholar
  9. Nourse, J. G. (1981),The Simple Solution to Rubik's Cube, New York: Bantam Books.Google Scholar
  10. Simon, H. A. (1983), ‘Search and Reasoning in Problem Solving’,Artificial Intelligence 21, pp. 7–29.Google Scholar

Copyright information

© Kluwer Academic Publishers 1992

Authors and Affiliations

  • H. Altay Guvenir
    • 1
  • Varol Akman
    • 1
  1. 1.Dept. of Computer Engineering and Information ScienceBilkent UniversityAnkaraTurkey

Personalised recommendations