Some statistical methods in machine intelligence research

Abstract About a dozen examples are given of the use of statistical methods in research on machine intelligence, most, though not all, previously known, but not previously brought together. The topics include the application of rationality to the research as a whole; the trading of immediate gain for information; adaptive control without the identification of a model, by using smoothing techniques; phoneme recognition using distinctive features and their derivatives; the compiling of dictionaries; “botryology” or concept formation by clump finding; information retrieval; medical diagnosis; game playing and its relationship to theorem proving; design of an alphabet or of a vocabulary; and artificial neural networks. Among the statistical themes that are emphasized are the estimation of probabilities; the use of amounts of information and of evidence as substitutes for utility when utility is difficult to estimate; decision trees; “evolving” probabilities; and maximum, minimum, and minimax entropy in diagnosis. In this survey of methods it has been necessary at several points to make do with references to the literature.

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