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Dissertation Information for Amit Basu

NAME:
- Amit Basu

DEGREE:
- Ph.D.

DISCIPLINE:
- Management

SCHOOL:
- University of Rochester (USA) (1986)

ADVISORS:
- None

COMMITTEE MEMBERS:
- None

MPACT Status: Incomplete - Not_Inspected

Title: IMPRECISE REASONING IN INTELLIGENT DECISION SUPPORT SYSTEMS

Abstract: 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.

This research shows how IDSS can be developed for unstructured problems even when the available knowledge is imprecise, and also demonstrates the versatility of such a system. For instance, the imprecision measures provide useful bases for comparing alternative solutions, even solutions that are "close misses"; evaluation of solutions is also possible for each subproblem. Information about imprecision can be used not only to interpret solutions, but also to control the problem solving process itself. Furthermore, the generation of useful results is often possible even if some of the available information is highly imprecise, sometimes even if some information is missing. Such features can be very useful in supporting unstructured decision making, yet cannot readily be supported by a system limited to precise reasoning.

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