This writing lays the foundation for a model of natural human reasoning with imprecise linguistic information. Key to the 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 doe s not require the use of fuzzy sets for the interpretation of linguistic terms and (2) the meanings of logical inferences are given as algorithms that act directly on terms themselves rather than on their underlying interpretations. Thus this work constitutes a return to the more purely symbolic or axiomatic 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 that encompasses both styles of reasoning. Two distinct types of logical inference are proposed, together with two associated modes of evidence combination. Final sections sketch the design of a backward-chaining algorithm through which the various inference types can be employed in diagnostic or, more generally, classificational reasoning systems. The algorithm is expected to be computationally manageable and therefore amenable to implementation in a functioning inference engine.
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Apr 1, 1992
Robin Fox 
