Fuzzy Truth Values Research Articles

Fundamentals of Fuzzy Prolog

In this paper, a Fuzzy Prolog is introduced that is based on the fuzzy resolution principle. There are three levels of concern: (1) the fuzzy truth values of predicates that depend on several values of individual variables, (2) the weights of rules that depend on several fuzzy truth values of premise and conclusion, and (3) the confidence of resolvent that depends on the fuzzy resolution principle. With these three levels, we introduce fuzziness into the resolution principle and unification and cause fuzzy logical inference to be significant in Fuzzy Prolog when the values of all variables are taken in the closed interval [0, 1].

Induction of inference rules for expert systems

Many of the decision-making processes of experts may be represented in terms of the fuzzy sets that correspond to their analysis of situations in terms of entities and their relationships. The elicitation of these fuzzy sets from experts can be carried out automatically by computer-based interview techniques. From this data the inference relations used in decision may be derived and encoded for an expert system shell. This paper analyses logical cluster-analytic techniques for inducing these inference relationships and gives examples of their applications to elicited data. The relations induced involve trade-offs between fuzzy truth values and the probability of their being correct. An information-theoretic measure of the uncertainty reduction due to a hypothesized relation is used to determine the optimum trade-off and to compare hypotheses.

Sketching: Estimating object positions from relational descriptions

One of the long-standing research problems in computer vision is the conversion of spatial information between the concrete domain of position in an image or in coordinate space and the more abstract domain of symbolic scene descriptions. This paper discusses a technique for solving the layout problem, one side of this general conversion problem. From a scene description composed of a set of symbolic spatial relations with fuzzy truth values, estimates of the objects' positions are derived. The position estimates consist of coordinate intervals with fuzzy truth values expressing the confidence associated with each interval.

A critical assessment of truth functional modification and its use in approximate reasoning

Abstract We examine the inconsistencies in the current notion of fuzzy truth values and show how these have arisen from an attempt to equate two separate concepts. We argue that any approximate reasoning system based on truth functional modification is redundnat and inefficient.

A new approach to approximate reasoning using a fuzzy logic

An approach to approximate reasoning based upon fuzzy logic is given. The method has the advantage of being computationally very simple and efficient even for multiple compound implication statements. This efficiency is the result of always processing ‘implication linguistic statements’ in a simple fuzzy truth space, so allowing for easy pencil and paper calculation. New concepts introduced in this paper are: 1. (i) the definition of a new set of fuzzy truth values 2. (ii) inverse truth functional modification for calculating fuzzy truth value restrictions 3. (iii) approximate reasoning using fuzzy truth value restrictions and an implication composition in truth space.