Titlebook: AI and Cognitive Science ’89; Dublin City Universi Alan F. Smeaton,Gabriel McDermott Conference proceedings 1990 British Computer Society 1

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Pasquale Fotia,Massimiliano Ferrara toward the acquisition of subject-matter knowledge contents. This expert system is based on a subject-matter model, a student model and heuristics allowing a teaching plan to be elaborated. This latter constitutes a dynamic learning framework for a given student. It will allow the subject-matter co

legacy

https://doi.org/10.1007/978-3-540-39972-8ate dialogue, for example by communicating priority, as well as communicating message content (e.g. head nodding) in complementary redundancy to the verbal flow. Communication lacking such a component will have a restricted range of efficiency and effectiveness, especially with regard to the pragmat

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Independent Component Analysis,wers queries in less than 2.5 seconds. The program accepts input in the form of typed English queries and answers queries queries on over 40 commands. OSCON is intended to be a consultant for various types of users who may ask vague and detailed queries. OSCON is programmed in Quintus Prolog. Unique

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光亮

https://doi.org/10.1007/978-1-4471-0249-6hips in the domain to determine which facts are relevant to particular goals. This information in turn is used to assist in the selection of which goal to work on next. Rather than being supplied by the user, the exclusive-or relationships are extracted directly from an analysis of the planning oper

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https://doi.org/10.1007/978-1-4471-0249-6zations of logic for that case. Nils J. Nilsson [1] presented a semantical generalization of logic in which the truth values of sentences are probability values between 0 and 1. This generalization applies to any logical system for which the consistency of a finite set of sentences can be establishe

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https://doi.org/10.1007/978-1-4471-0249-6evelopment process but also in their approach. This is the aim of the system we are proposing. It is based on approaches to programming arising from interviews with programmers working in an industrial environment. It includes Artificial Intelligence techniques to facilitate its implementation..The

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外来

https://doi.org/10.1007/978-1-4471-0249-6idence). However, uncertain relationships between evidence and hypotheses bearing on this evidence are difficult to represent in applications of the theory. Yen [1] extended the theory by introducing a probabilistic mapping that uses conditional probabilities to express these uncertain relationships

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Linear inverse theory with a priori data,notably rate of convergence and the size of the training set. Suggestions are made as to how improved gradient methods and the use of autoassociative networks together with cluster analysis techniques may address these difficulties.