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BBN Laboratories Incorporate A Subsidiary of Bolt fl', rank and Newman Inc. A)-A169 725 Report No. 6277 Progressions of Qualitative Models as a Foundation for Intelligent Learning Environments. S

5 S B. A. White, B. A. Frederickson, John R. Frederickson. The Evolution of Quantitative Models in Into- net Learning Environments I. Qualitative Models as Frameworks for Intelligent Learning Environments Chapter 4. The Case of Learning Environments 685 1. Background 2. A Framework for the Analysis of Data with Equivalent Models 3. Comparison of Qualitative Models vs. Equivalently Modeled Data 4. The Use of Modeling Theory to Explore the Interplay of Models and Data 5. The Comparison of Qualitative Models with Equip- eternally Modeled Data 6. Establishing the Structure of Qualitative Models 687 (cont'd) B. A. White, and John R. Frederickson. The Evolution of Qualitative Models in Into- net Learning Environments I. Qualitative Models as Frameworks for Intelligent Learning Environments 1. Background I. Qualitative Models as Frameworks for Intelligent Learning Environments This article is one of a set of papers prepared for the United States Army Research Institute in Ft. Pachuca, Arizona, as part of an ongoing project by the Bibliography of Computer and Information Science, Stanford University, directed by Dr. Barbara K. White, professor of computer science in the Faculty of Sciences and engineering of the University of California in San Diego. Dr. White has been working with Dr. John R. Frederickson, chair of the Department of Computer Sciences at the University of Arizona on the problem of how to find better ways of describing artificial information-theory devices in terms of meaningful mathematics. A significant portion of the time of the project has been consumed by an attempt to understand the implications for the future of the current computer-based training of intelligence systems. To this end the group developed the idea of a computerized training model, including a framework for the study of statistical properties of the model. In a second phase of the project the group investigated the use of a mathematical framework, called Bayesian statistics, in the study of artificial information-theory models and their interaction with artificial learning environments. In the third phase research results were presented and discussed at the American Physical Society meeting in Chicago on the problem of building a knowledge base that would make information available in this new information-based environment. 1. Background The problem that has been confronted at Ft.

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