A Methodological Framework for  Multistrategy Cooperative Learning

A Methodological Framework for Multistrategy Cooperative Learning (1990)

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by Ryszard S. Michalski

Venue:	PROCEEDINGS OF THE FIFTH INTERNATIONAL SYMPOSIUM ON METHODOLOGIES FOR INTELLIGENT SYSTEMS, KNOXVILLE, (ELSEVIER PUB
Citations:	5 - 4 self

BibTeX

@INPROCEEDINGS{Michalski90amethodological,
    author = {Ryszard S. Michalski},
    title = {A Methodological Framework for Multistrategy Cooperative Learning},
    booktitle = {PROCEEDINGS OF THE FIFTH INTERNATIONAL SYMPOSIUM ON METHODOLOGIES FOR INTELLIGENT SYSTEMS, KNOXVILLE, (ELSEVIER PUB},
    year = {1990},
    publisher = {}
}

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Abstract

This paper outlines basic assumptions and a theoretical basis for multistrategy task.adaptive learning (MTL) methodology, which aims at ultimately integrating a spectrum of learning strategies, such as empirical teaming, constructive induction, abduction, analytic learning. learning by analogy, and reinforcement learning. In MTL, in response to an input, a learner deternines the su'ategy, or a combination of su'ategies, that is mo. st appropriate for the learning task. This detemination is based on the relationship between the input, the leamegs background knowledge and the leamer's task. By means of a simple example we show how an MTL learner can employ, depending on the above relationship, emprical learning, constructive inductive generaiiz. afion, abduction, explanation-based learning and abstraction.

Citations

637	A theory and methodology of inductive learning - Michalski - 1983
284	Qualitative Results Concerning the Utility of Explanation-Based Learning - Minton - 1990
120	Using Genetic Algorithms to Solve NP-Complete Problems. 3rd Int. Conference on Genetic Algorithms - Jong, Spears - 1989
71	S.: The Logic of Plausible Reasoning: A Core Theory - Collins, Michalski - 1989
28	Toward a Unified Theory of Learning: Multistrategy Task-Adaptive Learning," Reports of Machine Learning and Inference Laboratory - Michalski - 1990
27	Acquiring Effective Search Control Rules: Explanation-Based Learning - Minton, Carbonell, et al. - 1987
18	The Use of Explanations for Similarity-Based Learning - Danyluk - 1987
18	Integrating Explanation-Based and Empirical Learning Methods in OCCAM - Pazzani - 1988
6	On the nature of explanation or Why did the wine bottle shatter, A paper accepted for - Michalski, H - 1988
4	Constructive Closed-loop Learning: Fundamental Ideas and Examples, a paper submitted for presentation at - Michalski, Watanabe
3	Assembly Planning Through Constructive Learning Approac.h," to appear - Ko - 1990
3	Types of Explanation and Their Role n Constructive Learning." to appear in Machine Learning and Inference Reports - Ko, Michalski - 1990
2	An Inductive Approach to Solving the Imperfect Theory Problem,"Proceedings of 1988 Symposium on Explanation-Based Learning - Dieuerich, laran, et al. - 1988
2	Plausible Reasoning: An Outline of Theory and Experiments - Michalski, Boehm-Davis, et al.
2	Multistrategy Constructive Learning: Toward a Unified Learning paradigm," an invited paper the - Michalski - 1989
1	xplaualion-Based Learning: An Alternative View,"Machine Learrffng - Deong, Mooney, et al. - 1986
1	Integraled Laming: Controlling Explanation - lbowitz - 1986
1	Explanation-Based Genera!ization: A Undying View - Mitchell, Keller, et al. - 1986
1	Theory Formation by Abduction: Initial Results of a Cas Study Based on the Chemical Revolution - S, Schulenburg - 1989