Abstracts of Current Literature

ABSTRACTS OF CURRENT LITERATURE Selected Dissertation Abstracts Compiled by: Susanne M. Humphrey National Library of Medicine Bethesda, MD 208!)4 Bob Krovetz University of Massachusetts Amherst, MA 0100.2 The following are citations selected by title and abstract as being related to computational linguistics or knowledge representation, resulting from a computer search, using the BRS Information Technologies retrieval service, of the Dissertation Abstracts International (DAI) database produced by University Microfilms International. Included are the UM order number and year-month of entry into the database; author; university, degree, and, if available, number of pages; title; DAI subject category chosen by the author of the dissertation; and abstract. References are sorted first by DAI subject category and second by author. Citations denoted by an MAI reference do not yet have abstracts in the database and refer to abstracts in the published Masters Abstracts International. Unless otherwise specified, paper or microform copies of dissertations may be ordered from University Microfilms International, Dissertation Copies, Post Office Box 1764, Ann Arbor, MI 48106; telephone for U.S. (except Michigan, Hawaii, Alaska): 1-800-521-3042, for Canada: 1-800-268-6090. Price lists and other ordering and shipping information are in the introduction to the published DAI. An alternate source for copies is sometimes provided at the end of the abstract. The dissertation titles and abstracts contained here are published with permission of University Microfilms International, publishers of Dissertation Abstracts International (copyright 1987,1986,1985) by University Micro- films International, and may not be reproduced without their prior permission. Imprecise Reasoning in Intelligent Decision Support Systems DAI V47(12), SecA, pp4439 Basu, Amit The University of Rochester Ph.D. 1986, 180 pages Business Administration, Management University Microfilms International ADG87-08218 In this thesis, a formal methodology to support reasoning with imprecise knowledge in computer-based decision support systems is developed. Many important decision problems are highly unstructured, and cannot be solved adequately using preset algorithms. Much of the complexity of such problems lies in the reasoning needed to determine how to solve individual problem instances. Existing decision support systems do not provide much reasoning support, largely due to the difficulty of representing and manipulating the fragmented and imprecise knowledge that is generally available. The methodology developed in this dissertation provides a basis for the design of Intelligent Decision Support Systems (IDSS) in which heuristic problem solving methods can be used to support reasoning as well as data retrieval and numerical computation. The dissertation consists of three parts. First, a logic based framework for reasoning is developed. The basic constructs of First Order Logic (FOL) are supplemented with constructs and mechanisms for automatic model manipulation, resulting in a powerful framework for IDSS development. Next, the need to distinguish between two different sources of imprecision, namely fuzziness and uncertainty is established, and methods for formally representing and mechanically manipulating fuzzy and/ or uncertain knowledge within the logic framework are developed. Finally, the strengths of the imprecise reasoning methodology are demonstrated by implementing a prototype IDSS to support imprecise reasoning and examining the prototype's performance on sample problems. i Computational Linguistics, Volume 15, …

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