International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

Fuzzy implication operators have been proposed as a tool for measuring the strength of connections between recorded concepts. Hasse diagrams are used to graphically illustrate the sometimes complicated relationships between such concepts. Establishing measures of significance for fuzzy implications is the focus of this paper. Lower and upper bounds for mean implication using the Kleene-Dienes operator are established. Examples of the intended use of these measures are given.

This paper presents a selection of alternative approaches to handle classification problems using the paradigm of case-based reasoning with fuzzy concepts. Our main concern is classification and pattern recognition queries in a fuzzy environment. An example was developed to explain the various methods and the results compared.

A general model for Fuzzy Linear Programming problem is studied. Fuzzy numbers generated by an homogeneous linear fuzzy function have been used for representing the imprecision of the parameters. A solution method is proposed using fuzzy numbers ranking procedures.

Within the many-valued approach for approximate reasoning, the aim of this paper is two-fold. First, to extend truth-values lattices to cope with the imprecision due to possible incompleteness of the available information. This is done by considering two bilattices of truth-value intervals corresponding to the so-called weak and strong truth orderings. Based on the use of interval bilattices, the second aim is to introduce what we call partial many-valued logics. The (partial) models of such logics may assign intervals of truth-values to formulas, and so they stand for representations of incomplete states of knowledge. Finally, the relation between partial and complete semantical entailment is studied, and it is provedtheir equivalence for a family of formulas, including the so-called free well formed formulas.

This paper introduces sensors employing a fuzzy numeric to symbolic interface. The fundamental design considerations for this kind of fuzzy symbolic sensor, or fuzzy sensor, are formally presented. Then, the use of these components for fuzzy control is discussed and illustrated.

This paper presents some results concerning the qualitative behaviour of possibilistic networks. The behaviour of singly connected networks is analysed, providing the foundations for qualitative reasoning about changes in possibility values in both predictive and evidential directions. The problems inherent in handling multiply connected networks are also discussed, and a possible solution is proposed. The behaviour of qualitative possibilistic networks is compared to qualitative probabilistic networks, and an example of the kind of reasoning that is permitted by the use of these networks is provided.

We propose a new iterative method for inducing classification knowledge from symbolic data. Our method generates a hierarchy of clusters and does not assume a particular knowledge representation formula (e.g., a conjunctive formula, a linear discriminant function). Our method consists of two stages, i.e., the optimization and clustering stages. The first stage maps the symbolic problem into the numerical domain based on an optimization approach. In the second stage, the examples are clustered into positive, negative, and fuzzy zones using induced membership degrees. This learning procedure is iterated until the fuzzy zone becomes empty. Out method learns "topological knowledge" which is found to be useful for the evaluation of the training data quality. Further, we show that our method is useful using real world data of an industrial knowledge acquisition problem.

We discuss the issue of weighted Min and Max aggregation. We provide for a formalization of the process of importance weighted transformation. The transformation of zero importance items to the identity element of the aggregation is emphasized. Median type aggregation is discussed and we describe its properties. We suggest a new technique for weighted median aggregation. We show its usefulness as an alternative the weighted average when we are using an ordinal scale.

In this paper concepts for goal-oriented reasoning within the blackboard development environment QBB are presented. The architecture of QBB supports the selection of problem solving actions with respect to the achivement of quality goals. Furthermore, interactions of goals are explicitly taken into account in action selection.

The features of QBB to support goal-oriented reasoning are presented. Especially, it is described how mutual influence of actions with respect to goal achievement can explicitly modeled as relationships between actions, the so-called compensation relations. The usefullness of compensation relations has been tested by goal-oriented modeling of the travelling salesman problem.

In this paper we will show that the different definitions of contradiction obtained in the literature can be modeled by means of an incompatibility relation.