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Backtrack searching in the presence of symmetry
Brown, C. | Finkelstein, L. | Purdom, P.
In Mora, T. (Ed.), Applied Algebra, Algebraic Algorithms and Error-Correcting Codes, pp. 99–110. Springer-Verlag.
Preliminary steps toward a taxonomy of problem-solving methods
In Marcus, S., (Ed.), Automating Knowledge Acquisition for Knowledge Based Systems, chapter 8, pages 120-146. Boston: Kluwer Academic Publishers
Issues in the development of a blackboard-based schema system for image understanding
Draper, B. A. | Collins, R. T. | Brolio, J. | Hanson, A. R. | Riseman, E. M.
In R. Engelmore and T. Morgan (Eds.), Blackboard systems. Wokingham, England: Addison-Wesley
Neural net pruning—Why and how
In IEEE International Conference on Neural Networks, pp. 325–333.
Learning to predict by the methods of temporal difference
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
The Rise of the Expert Company: How Visionary Companies Are Using Artificial Intelligence to Achieve Higher Productivity and Profits
Feigenbaum, E. A. | McCorduck, P. | Nii, H. P.
"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.
Explanation-based generalisation = partial evaluation
Artificial Intelligence, 36 (3): 401-12