Automated Theorem Proving in the Theory of Approximate Reasoning, with Applications to Logic Programming and Expert Systems.

摘要

The primary objective of this project is to develop a new model of natural human reasoning with imprecise linguistic information. Key to this model is a collection of abstraction mechanisms based on the concept of a linguistic variable, which was first introduced for this purpose within the context of a semantics based on fuzzy sets. The present approach differs from the earlier one, however, in that (1) it does not require the use of fuzzy sets for the interpretation of linguistic terms, and (2) the meanings of logical inferences are given as algorithms which act directly on terms themselves, rather than on their underlying interpretations. Thus this work constitutes a return to the more purely symbolic or aziomatic representations of logical deduction, whereas the fuzzy-sets model concerns denotational or semantic representations. The new model should not be viewed as a negation of the earlier approaches, however, but as an augmentation of them. The present work is intended as the beginning of a larger system which encompasses both styles of reasoning. Also underway is development of a prototype expert system shell which implements the new model, together with some of the earlier forms of fuzzy inference. This part of the work is being undertaken in part to identify the problems associated with the task of implementation and in this manner demonstrate that implementation is indeed possible. Development of the prototype is also desired so as to experiment with the model and determine its viability as a basis for reasoning in specific problem domains.