Can Machine Learning Offer Anything to Expert Systems
TLDR
Today’s expert systems have no ability to learn from experience, and learning capabilities are needed for intelligent systems that can remain useful in the face of changing environments or changing standards of expertise.Abstract:
Today’s expert systems have no ability to learn from experience. This commonly heard criticism, unfortunately, is largely true. Except for simple classification systems, expert systems do not employ a learning component to construct parts of their knowledge bases from libraries of previously solved cases. And none that I know of couples learning into closedloop modification based on experience, although the SOAR architecture [Rosenbloom and Newell 1985] comes the closest to being the sort of integrated system needed for continuous learning. Learning capabilities are needed for intelligent systems that can remain useful in the face of changing environments or changing standards of expertise. Why are the learning methods we know how to implement not being used to build or maintain expert systems in the commercial world?read more
Citations
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A genetic algorithm for generating fuzzy classification rules
Yufei Yuan,Huijun Zhuang +1 more
TL;DR: A Fuzzy Genetic Algorithm is developed to generate fuzzy classification rules using several techniques such as multi-value logic coding, composite fitness function, viability check, and rule extraction to improve the efficiency and the effectiveness of the algorithm.
Journal ArticleDOI
Cross-national comparisons of complex problem-solving strategies in two microworlds.
TL;DR: This study analyzes the CPS process by investigating thinking-aloud protocols in five countries and showed modification of the theoretical CPS model, task dependence of CPS strategies, and cross-national differences in CPS strategies.
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The usefulness of a machine learning approach to knowledge acquisition
TL;DR: It is clear that all machine learning methods used for knowledge acquisition should be replaced by other methods of rule induction that will generate complete sets of rules.
Journal ArticleDOI
Theoretical performance of genetic pattern classifier
TL;DR: In this paper, the authors investigated the behavior of a genetic-algorithm-based pattern classification methodology for an infinitely large number of training data points n,i n anN-dimensional space RN.
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Classification Accuracy: Machine Learning vs. Explicit Knowledge Acquisition
Arie Ben-David,Janice Mandel +1 more
TL;DR: There is evidence that machine learning models can provide better classification accuracy than explicit knowledge acquisition techniques and the main contribution of machine learning to expert systems is not just cost reduction, but rather the provision of tools for the development of better expert systems.
References
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TL;DR: This paper proposed a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization, which is illustrated in the context of several example problems, and used to contrast several existing systems for explanation based generalization.
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TL;DR: This book solves different problems like resolving ambiguities in word meanings, finding analogies between things, making logical and nonlogical inference, resolving inconsistency in information engaging in coherent discourse with a person and more.
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TL;DR: This paper discusses programs to manipulate in a suitable formal language (most likely a part of the predicate calculus) common instrumental statements, where the basic program will draw immediate conclusions from a list of premises.