Summary
Learning Search Control Knowledge is a well-written and thorough discussion of both the nature of explanation-based learning and its application to problem-solving. The discussion is replete with detail, stocked with examples and well-supported by empirical evidence. It is recommended both for its comprehensive overview and comparison of EBL-based efforts and for its detailed discussion of EBL implementation issues and techniques. This is the clearest and most understandable description of EBL theory and implementation which I have encountered. Newcomers will welcome its overview of EBL and its literature; practitioners will find the discussion of the learning system refreshingly clear. The result is a well-rounded discussion with something to offer to everyone concerned with machine learning.
Article PDF
References
AgreP.E. (1988).The dynamic structure of everyday life (Technical Report AI-TR 1085). Cambridge, MA: MIT Artificial Intelligence Laboratory.
AltermanR. (1988). Adaptive planning.Cognitive Science Journal,12, 393–421.
Alterman, R., & Zito-Wolf, R. (1990). Planning and understanding: Revisited.Proceedings of 1990 AAAI Spring Symposium.
Bjorner, D., Ershov, A.P., & Jones, N.D. (1988).Partial evaluation and mixed computation. North-Holland.
CarbonellJ.G., & GilY. (1987). Learning by experimentation.Proceedings of the Fourth International Workshop on Machine Learning. Cambridge, MA: Morgan Kaufmann.
DeJongG., & MooneyR. (1986). Explanation-based learning: An alternative view.Machine Learning,1, 145–176.
Etzioni, O. (1990). Why PRODIGY/EBL works.Proceedings of the Eighth National Conference on Artificial Intelligence (pp. 916–922).
FikesR.E., & NilssonN.J. (1971). STRIPS: A new approach to the application of theorem proving to problem solving.Artificial Intelligence 2, 189–208.
Hammond, K.J. (1986). CHEF: A model of case-based planning.Proceedings of AAAI-86 (pp. 267–271).
IbaG.A. (1989). A heuristic approach to the discovery of macro-operators.Machine Learning,3, 285–317.
Kahneman, D., Slovic, P., & Tversky, A. (1982).Judgement under uncertainty: Heuristics and biases. Cambridge University Press.
Kedar-CabelliS.T., & McCartyL.T. (1987). Explanation-based generalization as resolution theorem-proving.Proceedings of the Fourth International Workshop on Machine Learning (pp. 383–389). Cambridge, MA: Morgan Kaufmann.
KolodnerJ.L., & SimpsonR.L. (1989). The MEDIATOR: Analysis of an early case-based problem solver.Cognitive Science,13, 507–549.
KorfR.E. (1985). Macro-operators: A weak method for learning.Artificial Intelligence,26, 35–77.
LairdJ.E., RosenbloomP., & NewellA. (1986). Chunking in SOAR: The anatomy of a general learning mechanism.Machine Learning,1, 11–46.
MintonS. (1988).Learning search control knowledge: An explanation-based approach (Technical Report CMU-CS-88–133). Pittsburgh, PA: Carnegie-Mellon University.
MitchellT., KellerR., & Kedar-CabelliS. (1986). Explanation-based generalization: A unifying view.Machine Learning,1, 47–80.
PazzaniM.J. (1988).Learning causal relationships: An integration of empirical and explanation-based learning methods (Technical Report UCLA-AI-88–10). Los Angeles, CA: University of California, Los Angeles.
Prieditis, A.E., & Mostow, J. (1987). PROLEARN: Toward a Prolog interpreter that learns.Proceedings of AAAI-87 (pp. 494–498).
Schank, R. (1982).Dynamic memory, Cambridge University Press.
SmithD.A., & HickeyT. (1990).Partial evaluation of CLP(FT) (Technical Report CS-90–148). Waltham, MA: Brandeis University.
Suchman, L.A. (1987).Plans and situated actions. Cambridge University Press.
vanHarmelanF., & BundyA. (1988). Explanation-based generalisation = Partial evaluation.Artificial Intelligence,36, 401–412.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Zito-Wolf, R.J. Learning search control knowledge: An explanation-based approach. Mach Learn 6, 197–204 (1991). https://doi.org/10.1007/BF00114164
Issue Date:
DOI: https://doi.org/10.1007/BF00114164