Conceptual Graphs And Fuzzy Logic
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Conceptual Graphs and Fuzzy Logic
In this volume, first we formulate a framework of fuzzy types to represent both partial truth and uncertainty about concept and relation types in conceptual graphs. Like fuzzy attribute values, fuzzy types also form a lattice laying a common ground for lattice-based computation of fuzzy granules. Second, for automated reasoning with fuzzy conceptual graphs, we develop foundations of order-sorted fuzzy set logic programming, extending the theory of annotated logic programs of Kifer and Subrahmanian (1992). Third, we show some recent applications of fuzzy conceptual graphs to modelling and computing with generally quantified statements, approximate knowledge retrieval, and natural language query understanding.
The State of the Art in Computational Intelligence
Author: Peter Sincak
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-11-14
Computational Intelligence is a very dynamic domain of modern information society which integrates fields such as neural networks, fuzzy systems, evolutionary computation and intelligent systems in general. The book presents papers from the Euro-International Symposium on Computational Intelligence held in Kosice (Slovak Republic) in August 2000. It contains theoretical studies along with a chapter on applications and case studies. One of the main results of the symposium is that the combination of various techniques into hybrid intelligent systems will be very important for the development of intelligent information systems in the 21st century. The book also contains interesting forewords written by L.A. Zadeh, D.E. Goldberg, and K. Fukushima.
Conceptual Graphs for Knowledge Representation
Author: Guy W. Mineau
language: en
Publisher: Springer Science & Business Media
Release Date: 1993-07-14
Artificial Intelligence and cognitive science are the two fields devoted to the study and development of knowledge-based systems (KBS). Over the past 25years, researchers have proposed several approaches for modeling knowledge in KBS, including several kinds of formalism such as semantic networks, frames, and logics. In the early 1980s, J.F. Sowa introduced the conceptual graph (CG) theory which provides a knowledge representation framework consisting of a form of logic with a graph notationand integrating several features from semantic net and frame representations. Since that time, several research teams over the world have been working on the application and extension of CG theory in various domains ranging from natural language processing to database modeling and machine learning. This volume contains selected papers fromthe international conference on Conceptual Structures held in the city of Quebec, Canada, August 4-7, 1993. The volume opens with invited papers by J.F. Sowa, B.R. Gaines, and J. Barwise.