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A great number of techniques have been applied to the general problem of adaptive control. What began as a study of engineering adaptive control problems involving dynamics, system and measurement noise, monitoring, transduction, and on-line instrumentation seems to have moved towards learning theory and methodology research that uses a refined plant/environment model as a vehicle of demonstration. An attempt is made to bring together, order, and briefly discuss many contributions in this field, bridging the era of earlier engineering practice to more recent artificial intelligence speculation. Both unimodal and multimodal strategies are discussed, together with problems arising in nonstationary environmental situations where information conservation, update, and retrieval are of considerable importance. Methods discussed include gradient, correlation, random, stochastic automata, fuzzy automata, pattern recognition, and mixed strategies. A selected reference list is provided.