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This article introduces a class of incremental learning procedures specializedfor prediction that is, for using past experience with an incompletely knownsystem to predict its future behavior. Whereas conventional prediction-learningmethods assign credit by means of the difference between predicted and actual outcomes,tile new methods assign credit by means of the difference between temporallysuccessive predictions. Although such temporal-difference method~ have been used inSamuel's checker player, Holland's bucket brigade, and the author's Adaptive HeuristicCritic, they have remained poorly understood. Here we prove their convergenceand optimality for special cases and relate them to supervised-learning methods. Formost real-world prediction problems, telnporal-differenee methods require less memoryand less peak computation than conventional methods and they produce moreaccurate predictions. We argue that most problems to which supervised learningis currently applied are really prediction problemsMachine Learning 3: 9-44, erratum p. 377

"This is a popular interpretation of expert systems - computer models providing artificial intelligence to managers and businessmen - which are transforming business in the 1980s. The book gives an inside view of many international companies such as Canon, Westinghouse, IBM, Du Pont and others." New York: Times Books.

"Expert systems are among the most exciting computer applications to emerge in the last decade. They allow a computer program to use expertise to assist in a variety of problems, such as diagnosing equipment failures and designing new equipment. Utilizing the results of artificial intelligence (AI) work on problem solving, they have become a commercially successful demonstration of the power of AI techniques. Correspondingly, by testing current AI methods in applied contexts, expert systems provide important feedback to the science about the strengths and limitations of these methods. In this review, we present the fundamental considerations in constructing an expert system, assess the state of the art, and indicate directions for future research. Our discussion focuses on the computer science issues, as opposed to issues of management or application." Annual Review of Computer Science.

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A set of hypotheses is formulated for a connectionist approach to cognitive modeling.

CACM, 31, 38–43.

Methods from computational group theory are used to improve the speed of backtrack searching on problems with symmetry. The symmetry testing algorithm, which is similar to a color automorphism algorithm, takes the symmetry group as input and uses it to avoid searching equivalent portions of the search space. The algorithm permits dynamic search rearrangement in conjunction with symmetry testing. Experimental results confirm that the algorithm saves a considerable amount of time on some search problems.

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This paper presents a framework for exploring issues in time-dependent planning: planning in which the time available to respond to predicted events varies, and the decision making required to formulate effective responses is complex. Our analysis of time-dependent planning suggests an approach based on a class of algorithms that we call anytime algorithms. Anytime algorithms can be interrupted at any point during computation to return a result whose utility is a function of computation time. We explore methods for solving time-dependent planning problems based on the properties of anytime algorithms. Time-dependent planning is concerned with determining how best to respond to predicted events when the time available to make such determinations varies from situation to situation.