Book Chapter10.1016/B978-0-08-050684-5.50011-2
Representing Attribute-Based Concepts in a Classifier System
Lashon B. Booker
- 01 Jan 1991
- Vol. 1, pp 115-127
33
TL;DR: This paper describes some straightforward binary encodings for attribute-based instance spaces that give classifier systems the ability to represent ordinal and nominal attributes as expressively as most symbolic machine learning systems, without sacrificing the building blocks required by the genetic algorithm.
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Abstract: Legitimate concerns have been raised about the expressive adequacy of the classifier language. This paper shows that many of those concerns stem from the inadequacies of the binary encodings typically used with classifier systems, not the classifier language per se. In particular, we describe some straightforward binary encodings for attribute-based instance spaces. These encodings give classifier systems the ability to represent ordinal and nominal attributes as expressively as most symbolic machine learning systems, without sacrificing the building blocks required by the genetic algorithm.
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XCS and the Monk's Problems
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The evolution of strategies for multiagent environments
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42
References
Classifier systems and genetic algorithms
TL;DR: The definition, theory, and extant applications of classifier systems are reviewed, comparing them with other machine learning techniques, and closing with a discussion of advantages, problems, and possible extensions of classifiers.
1K
The Need for Biases in Learning Generalizations
Tom M. Mitchell
- 01 Jan 2007
TL;DR: The notion of bias in generalization problems is defined, and it is shown that biases are necessary for the inductive leap.
Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms
Richard A. Caruana,J. David Schaffer +1 more
- 01 Jan 1988
TL;DR: Experimental results are presented that indicate Gray coding is generally superior to binary coding for function optimization using the genetic algorithm and suggests that Gray coding eliminates the “Hamming cliff” problem that makes some transitions difficult when using a binary representation.
263
Intelligent behavior as an adaptation to the task environment
Lashon Bernard Booker
- 01 Jan 1982
TL;DR: This dissertation argues that examining more closely the way animate systems cope with real-world environments can provide valuable insights about the structural requirements for intelligent behavior.
228
Active perception and reinforcement learning
Steven D. Whitehead,Dana H. Ballard +1 more
- 01 Jun 1990
TL;DR: This paper considers adaptive control architectures that integrate active sensory-motor systems with decision systems based on reinforcement learning and proposes a new decision system that overcomes the effects of perceptual aliasing.
160