# Graph-based induction as a unified learning framework

- 133 Downloads
- 41 Citations

## Abstract

We describe a graph-based induction algorithm that extracts typical patterns from colored digraphs. The method is shown to be capable of solving a variety of learning problems by mapping the different learning problems into colored digraphs. The generality and scope of this method can be attributed to the expressiveness of the colored digraph representation, which allows a number of different learning problems to be solved by a single algorithm. We demonstrate the application of our method to two seemingly different learning tasks: inductive learning of classification rules, and learning macro rules for speeding up inference. We also show that the uniform treatment of these two learning tasks enables our method to solve complex learning problems such as the construction of hierarchical knowledge bases.

## Key words

Machine learning induction graph## Preview

Unable to display preview. Download preview PDF.

## References

- 1.R.S. Michalski, “A theory and methodology of inductive learning,”
*Artif. Intell.*vol. 20, pp. 111–161, 1983.Google Scholar - 2.J.R. Quinlan, “Induction of decision trees,”
*Machine Learning*vol. 1, pp. 81–106, 1986.CrossRefGoogle Scholar - 3.T. Ellman, “Explanation-Based Learning: A survey of programs and perspectives,”
*ACM Comput. Surv.*vol. 21, no. 2, pp. 165–220, 1989.Google Scholar - 4.S. Mahadevan and J. Connell, “Automatic programming of behavior-based robots using reinforcement learning,”
*Artif. Intell.*vol. 55, pp. 311–365, 1992.Google Scholar - 5.B. Falkenhainer and K.D. Forbus, “Compositional modeling: finding the right model for the job,”
*Artif. Intell.*vol. 51, pp. 95–143, 1991.Google Scholar - 6.Z.Y. Liu and A.M. Farley, “Shifting ontological perspectives in reasoning about physical systems,” in
*AAAI-90*, Boston, MA, 1990, pp. 395–400.Google Scholar - 7.M. Lebowitz, “Integrated learning: controlling explanation,”
*Cog. Sci.*vol. 10, pp. 219–240, 1986.Google Scholar - 8.M. Pazzani, M. Dyer, and M. Flowers,” The role of prior causal theories in generalization,” in
*AAAI-86*, Philadelphia, PA, 1986, pp. 545–550.Google Scholar - 9.A.P. Danyluk, “The use of explanation for similarity-based learning,” in
*IJCAI-87*, Milan, Italy, 1987, pp. 274–276.Google Scholar - 10.H. Hirsh, “Combining empirical and analytical learning with version spaces,” in
*ML-89*, Ithaca, NY, 1989, pp. 29–33.Google Scholar - 11.P.S. Rosenbloom and J. Aasman, “Knowledge level and inductive uses of chunking (EBL),” in
*AAAI-90*, Boston, MA, 1990, pp. 821–827.Google Scholar - 12.O. Etzioni, “STATIC: A problem-space compiler for PRODIGY,” in
*AAAI-91*, Anaheim, CA, 1991, pp. 533–540.Google Scholar - 13.S.A. Vere, “Induction of relational productions in the presence of background information,” in
*IJCAI-77*, Cambridge, MA, 1977, pp. 349–355.Google Scholar - 14.J.R. Anderson and P.J. Kline, “A learning system and its psychological implications,” in
*IJCAI-79*, Tokyo, Japan, 1979, pp. 16–21.Google Scholar - 15.R. Levinson, “A self-organizing retrieval system for graphs,” in
*AAAI-84*, Austin, TX, 1984, pp. 203–206.Google Scholar - 16.N.S. Flann and T.G. Dietterich, “A study of explanation-based methods for inductive learning,”
*Machine Learning*, 1989, pp. 187–226.Google Scholar - 17.L.B. Holder, “Empirical substructure discovery,” in
*ML-89*, Ithaca, NY, 1989, pp. 133–136.Google Scholar - 18.L.B. Holder, D.J. Cook, and H. Bunke, “Fuzzy substructure discovery,” in
*ML-92*, Aberdeen, Scotland, 1992, pp. 218–223.Google Scholar - 19.T.M. Mitchell, R.M. Keller, and S.T. Kedar-Cabelli, “Explanation-based generalization: a unifying view,”
*Machine Learning*vol. 1, pp. 47–80, 1986.Google Scholar - 20.G. DeJong and R. Mooney, “Explanation-based learning: an alternative view,”
*Machine Learning*vol. 1, pp. 145–176, 1986.Google Scholar - 21.R.E. Korf, “Macro-operators: A weak method for learning,”
*Artif. Intell.*vol. 25, pp. 35–77, 1985.Google Scholar - 22.G.A. Iba, “A heuristic approach to the discovery of macro-operators,”
*Machine Learning*vol. 3, pp. 285–317, 1989.Google Scholar - 23.B. Kuipers, “Qualitative simulation,”
*Artif. Intell.*vol. 29, pp. 289–338, 1986.Google Scholar - 24.G.G. Towell, J.W. Shavlik, and M.O. Noordewier, “Refinement of approximate domain theories by knowledge-based neural networks,” in
*AAAI-90*, Boston, MA, 1990, pp. 861–866.Google Scholar - 25.B. Efron, “The jackknife, the bootstrap and other resampling plans,” in
*SIAM*, Bowling Green State University: Bowling Green, OH, 1982.Google Scholar - 26.L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone,
*Classification and Regression Trees*Wadsworth & Brooks/Cole Advanced Books & Software: Belmont, CA, 1984.Google Scholar - 27.S.M. Weiss and N. Indurkhya, “Reduced complexity rule induction,” in
*IJCAI-91*, Sydney, Australia, 1991, pp. 678–684.Google Scholar - 28.S. Yamada and S. Tsuji, “Selective learning of macro-operators with perfect causality,” in
*IJCAI-89*. Detroit, MI, 1989, pp. 603–608.Google Scholar - 29.C. Mead and L. Conway,
*Introduction to VLSI Systems*Addison-Wesley: Reading, MA, 1980.Google Scholar - 30.S. Arikawa, S. Miyano, and A. Shinohara, “Knowledge acquisition from amino acid sequences by learning algorithms,” in
*JKAW92*, Hatoyama, Japan, 1992, pp. 109–128.Google Scholar - 31.S. Minton, “Quantitative results concerning the utility of Explanation-Based Learning,”
*Artif. Intell.*vol. 42, pp. 363–391, 1990.Google Scholar