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Information Technology
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
Natural language interfaces
Perrault, Ray | Grosz, Barbara
This article describes TEAM, a transportable natural-language interface system. TEAM was constructed to test the feasibility of building a natural-language system that could be adapted to interface with new databases by users who are not experts in natural-language processing. An overview of the system design is presented, emphasizing those choices that were imposed by the demands of transportability. Several general problems of natural-language processing that were faced in constructing the system are discussed, including quantifier scoping, various pragmatic issues, and verb acquisition. TEAM is compared with several other transportable systems; this comparison includes a discussion of the range of natural language handled by each as well as a description of the approach taken to achieving transportability in each system.
Quantitative results concerning the utility of explanation-based learning
In AAAI- 88, pp. 564â569.
Quantifying inductive bias: AI learning algorithms and Valiantâs learning framework
36[#2]:177-221. Artificial Intelligence,
Search in Artificial Intelligence
Citing the confusing statements in the AI literature concerning the relationship between branch and bound (B&B) and heuristic search procedures were present a simple and general formulation of B&B which should help dispel much of the confusion. We illustrate the utility of the formulation by showing that through it some apparently very different algorithms for searching And/Or trees reveal the specific nature of their similarities and differences. In addition to giving new insights into the relationships among some AI search algorithms, the general formulation also provides suggestions on how existing search procedures may be varied to obtain new algorithms.