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A parallel network that learns to play backgammon
Artificial Intelligence, 39 (3), 357-90.
A survey of knowledge acquisition techniques and tools
Knowledge Acquisition, 1[#1]:3-37
Explanation-Based Learning: A Problem-Solving Perspective
Minton, S. | Carbonell, C. | Knoblock, D.R. | Kuokka, O.
Artificial Intelligence 40 (1), 63-118.
Propagating belief functions in qualitative Markov trees
Shafer, G. | Shenoy, P. P. | Mellouli, K.
International Journal of Approximate Reasoning,
Classifier systems and genetic algorithms
Booker, L. B. | Goldberg, D. E. | Holland, J. H.
ABSTRACT Classifier systems are massively parallel, message-passing, rule-based systems that learn through credit assignment (the bucket brigade algorithm) and rule discovery (the genetic algorithm). They typically operate in environments that exhibit one or more of the following characteristics: (1) perpetually novel events accompanied by large amounts of noisy or irrelevant data; (2) continual, often real-time, requirements for action; (3) implicitly or inexactly defined goals; and (4) sparse payoff or reinforcement obtainable only through long action sequences. Classifier systems are designed to absorb new information continuously from such environments, devising sets of compet- ing hypotheses (expressed as rules) without disturbing significantly capabilities already acquired. This paper reviews the definition, theory, and extant applications of classifier systems, comparing them with other machine learning techniques, and closing with a discussion of advantages, problems, and possible extensions of classifier systems. Artificial Intelligence, 40 (1-3), 235-82.
Graphical models for associations between variables, some of which are qualitative and some quantitative
The Annals of Statistics publishes research papers of the highest quality reflecting the many facets of contemporary statistics. Primary emphasis is placed on importance and originality, not on formalism. The discipline of statistics has deep roots in both mathematics and in substantive scientific fields. Mathematics provides the language in which models and the properties of statistical methods are formulated. It is essential for rigor, coherence, clarity and understanding. Consequently, our policy is to continue to play a special role in presenting research at the forefront of mathematical statistics, especially theoretical advances that are likely to have a significant impact on statistical methodology or understanding.