Abstract
This research examines the process of learning problem solving with minimal requirements for a priori knowledge and teacher involvement. Experience indicates that knowledge about the problem solving task can be used to improve problem solving performance. This research addresses the issues of what knowledge is useful, how it is applied during problem solving, and how it can be acquired. For each operator used in the problem solving domain, knowledge is incrementally learned concerning why it is useful, when it is applicable, and what transformation it performs. The method of experimental goal regression is introduced for improving the learning rate by approximating the results of analytic learning. The ideas are formalized in an algorithm for learning and problem solving and demonstrated with examples from the domains of simultaneous linear equations and symbolic integration.
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Amarel, S. (1968), On representations of problems of reasoning about actions. In D. Michie (Ed. ), Machine Intelligence3. Edinburgh University Press.
Anzai, Y. (1978). Learning strategies by computer. Proceedings of the Second Canadian Society for the Computational Study of Intelligence (pp. 181-190). Toronto, Canada.
Brazdil, P. (1978). Experimental learning model. Proceedings of the Conference on Artificial Intelligence and Simulation of Behaviour (pp. 46-50). Hamburg, West Germany.
DeJong, O. (1981). Generalization based on explanations. Proceedings of the Seventh International Joint Conference on Artificial Intelligence (pp. 67-69). Vancouver, Canada.
DeJong, G. (1983). Acquiring schemata through understanding and generalizing plans. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (pp. 462-464). Karlsruhe, West Germany.
Dijkstra, E. (1975). Guarded commands, non-determinancy and formal derivation of programs. Com-munications of the Association for Computing Machinery 18, 453-457.
Iba, G. (1985). Learning by discovering macros in puzzle solving. Proceedings of the Ninth International Joint Conference on Artificial Intelligence (pp. 640-643). Los Angeles, California.
Kibler, D., & Porter, B. (1983a). Perturbation:A means for guiding generalization. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (pp. 415-418). Karlsruhe, West Germany.
Kibler, D., & Porter, B. (1983b). Episodic learning. Proceedings of the Third National Conference on Ar-tificial Intelligence (pp. 191-196). Washington, D. C.
Langley, P. (1980). Finding common paths as a learning mechanism. Proceedings of the Third Conference of the Canadian Society for Computational Studies of Intelligence (pp. 12-19). Victoria, B. C., Canada.
Langley, P. (1983). Learning effective search heuristics. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (pp. 419-421). Karlsruhe, West Germany.
Langley, P., & Ohlsson, S. (1984). Automated cognitive modeling. Proceedings of the Fourth National Conference on Artificial Intelligence (pp. 193-197). Washington, D. C.
Mahadevan, S. (1985). Verification based learning:A generalization strategy for inferring problem reduction methods. Proceedings of the Ninth International Conference on Artificial Intelligence (pp. 616-623). Los Angeles, California.
Michalski, R. S. (1983). A theory and methodology of inductive learning. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, (Eds. ), Machine learning. Palo Alto: Tioga.
Minton, S. (1984). Constraint-based generalization:Learning game-playing plans from single examples. Proceedings of the Fourth National Conference on Artificial Intelligence (pp. 251-254). Austin, Texas.
Minton, S. (1985). Selectively generalizing plans for problem solving. Proceedings of the Ninth Interna-tional Conference on Artificial Intelligence (pp. 596-599). Los Angeles, California.
Mitchell, T. M., Utgoff, P. E., Nudel, B., & Banerji, R. (1983). Learning by experimentation:Acquiring and refining problem solving heuristics. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, (Eds. ), Machine learning. Palo Alto:Tioga.
Mitchell, T. M., Mahadevan, S., & Steinberg, L. I. (1985). LEAP:A learning apprentice for VLSI design. Proceedings of (he Ninth International Joint Conference on Artificial Intelligence (pp. 573-580). Austin, Texas.
Mitchell, T. M., Keller, R. M., & Kedar-Cabelli, S. T. (1986). Explanation-based generalization:A unifying view. Machine learning, 1, 47-80.
Newell, A., & Simon, H. A. (1961). The simulation of human thought. Current trends in psychological theory. Pittsburgh: University of Pittsburgh Press.
Neves, D. M. (1978). A computer program that learns algebraic procedures by examining examples and working problems in a textbook. Proceedings of the Second Canadian Society for the Computational Study of Intelligence (pp. 191-195). Toronto, Ontario.
Porter, B., & Kibler, D. (1984). Learning operator transformations. Proceedings of the Fourth National Conference on Artificial Intelligence (pp. 278-282). Austin, Texas.
Porter, B. (1984). Learning Problem Solving. PhD Dissertation, Department of Information and Computer Science, University of California, Irvine.
Porter, B., & Kibler, D. (1985). A comparison of analytic and experimental goal regression for machine learning. Proceedings of the Ninth International Joint Conference on Artificial Intelligence (pp. 555-559). Los Angeles, California.
Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1, 81-106.
Sammut, C., & Banerji, R. (1983). Hierarchical memories:an aid to concept learning. Proceedings of the Second International Machine Learning Workshop (pp. 74-80). Allerton House, Illinois.
Samuel, A. L. (1959).Some studies in machine learning using the game of checkers.IBM Journal of Research and Development 3.
Smith, E., & Medin, D. (1981). Categories and concepts. Cambridge: Harvard University Press.
Utgoff, P. (1983). Adjusting bias in concept learning. Proceedings of the Second International Machine Learning Workshop (pp. 105-109). Allerton House, Illinois.
Utgoff, P. (1984). Shift of Bias for Inductive Concept Learning. PhD Dissertation, Department of Computer Science, Rutgers University.
Vere, S. A. (1977). Induction of relational productions in the presence of background information. Pro-ceedings of the Fifth International Joint Conference on Artificial Intelligence (pp. 349-355). Cam-bridge, Massachusetts.
Waldinger, R. (1977). Achieving several goals simultaneously. In E. W. Elcock, and D. Michie, (Eds. ), Machine Intelligence 8. New York: Halstead and Wiley.
Winston, P. H. (1975). Learning structural descriptions from examples. In Winston, P. H. (Ed. ), The psychology of computer vision. New York: McGraw-Hill.
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Porter, B.W., Kibler, D.F. Experimental Goal Regression: A Method for Learning Problem-Solving Heuristics. Machine Learning 1, 249–285 (1986). https://doi.org/10.1023/A:1022806513480
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DOI: https://doi.org/10.1023/A:1022806513480