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Bayesian Learning

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BAYESIAN LEARNING Abstract Uncertainty has presented a difficult obstacle in artificial intelligence. Bayesian learning outlines a mathematically solid method for dealing with uncertainty based upon Bayes' Theorem. The theory establishes a means for calculating the probability an event will occur in the future given some evidence based upon prior occurrences of the event and the posterior probability that the evidence will predict the event. Its use in artificial intelligence has been met with success in a number of research areas and applications including the development of cognitive models and neural networks. At the same time, the theory has been criticized for being philosophically unrealistic and logistically inefficient. Bayesian Learning The aim of artificial intelligence is to provide a computational model of intelligent behavior (Pearl, 1988). Expert systems are designed to embody the knowledge of an expert in a given field. But how do people become experts themselves? While artificial intelligence can produce Ph.D. quality experts, a more difficult challenge lies in creating a naive observer. The common sense people use in everyday reasoning provides one of the most difficult challenges in building intelligent systems. Common sense reasoning is often based on incomplete knowledge and is powerfully broad in its use. Intelligent systems have historically been successful in specific domains with well defined structures. To make them succeed in a broad arena, they would need either a greater base of knowledge or be able to deal with uncertainty and learn. In light of the fact that the former option is more demanding in resources and assumes that all the appropriate knowledge is obtainable, the latter is an attr... ... middle of paper ... ...cess. References Anderson, J. R. (1993). Rules of the Mind. New Jersey: Lawrence Erlbaum Associates. Anderson, J. R. (1991). Is human cognition adaptive? Behavior and Brain Sciences, 14, 471-517. Krueger, L. E. (1984). Perceived numerosity: A comparison of magnitude production, magnitude estimation, and discrimination judgements. Perception and Psychophysics, 35(6), 536-542. McCarthy, J. & Hayes, P. (1969). Some philosophical problems from the stand point of artificial intelligence. In B. Metltzer & D. Michie (Eds.), Machine Intelligence Vol. 4, (pp. 463-502). Edinburgh, U.K.: Edinburgh University Press. Neal, R. M. (1996). Bayesian Learning for Neural Networks. New York: Springer-Verlag. Pearl, J. P. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, California, USA: Morgan Kaufmann Publishers, Inc..
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