NSF Award
9103925
This is the first year funding of a three year continuing grant. This research focuses on the development of a formal system, based upon possibility thoery for the representation and manipulation of uncertainity in expert systems. The use of Possibility theory allows a natural representation of linguistic information as well being an appropriate setting for handling partial matching. One emphasis of the research is the development of tools for the general representation of imprecise functional information. Here use is made of the ability possibility theory to interact with other knowledge representation formalisms such as default reasoning and probability theory. A second emphasis of this research is the development of a tool to help in the validation of knowledge-based systems containing uncertain information. Particular attention is paid to the discovery of potential inconsistencies in a knowledge base. The approach used is based upon the idea of reflecting back on the input. A very fundamental uncertainty principle is the specificity-correctness tradeoff. What this principle says is that in providing information we generally must make a tradeoff. What this principle says is that in providing information we generally must make a tradeoff between being very specific and running the risk of being incorrect or being unspecific and in turn assure ourselves of being correct. In most expert systems one desires both correctness and specificity. In this research the measure of specificity is extended to environments in which their exists as underlying similarity relation. The significance of this research is that it provides tools for builders of expert systems which can be used to formally and systemically include uncertainty in their systems.
