Logical models of arguement formalize commonsense reasoning while taking process and
computation seriously. This survey discusses the main ideas that characterize different …

This paper surveys methods for representing and reasoning with imperfect information. It
opens with an attempt to classify the different types of imperfection that may pervade data …

Handbook of Knowledge Representation describes the essential foundations of Knowledge
Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up …

Three main semantics for membership functions seem to exist in the literature: similarity,
preference and uncertainty. Each semantics underlies a particular class of applications …

Fuzzy rules have been advocated as a key tool for expressing pieces of knowledge in “fuzzy
logic”. However, there does not exist a unique kind of fuzzy rules, nor is there only one type …

We justify the need for computing both membership and typicality values when clustering
unlabeled data. Then we propose a new model called fuzzy-possibilistic c-means (FPCM) …

In recent years there has been a major trend in uncertainty (more specifically, partial belief)
modelling emphasizing the idea that the degree of confidence in an event is not totally …

This book aims to synthesize different directions in knowledge studies into a unified theory of
knowledge and knowledge processes. It explicates important relations between knowledge …

One of the most controversial issues in uncertainty modeling and information sciences is the
relationship between probability theory and fuzzy sets. The literature pertaining to this …

The work in this paper is directed towards sophisticated formalisms for reasoning under
probabilistic uncertainty in ontologies in the Semantic Web. Ontologies play a central role in …