Intelligent knowledge-based systems in uncertain environments

[phd thesis]

Full reference

M. Wallace, Intelligent knowledge-based systems in uncertain environments, Ph.D. Thesis, School of Electrical and Computer Engineering, National Technical University of Athens, 2005

Abstract

Uncertainty has gradually attained acceptance and a very distinct role in scientic thought as well as in the scientic view of the world. As far as intelligent knowledge based systems are concenred, uncertainty is present at all levels of their operation and its role is determinant of their eectiveness. In this thesis we propose a series of solutions to uncertainty related problems. In their turn, these solutions provide for further thought and progress in a series of directions.

In the first part of the thesis, which is also the lengthiest, the emphasis is on the semantics. In this framework, the important problems to consider are those of modelling real world concepts thus constructing a formal knowledge base and of exploiting the information contained in this knowledge base in practical applications, given its size. In this direction, chapter 2 proposes the utilization of fuzzy relations for the representation of knowledge and explains how this knowledge can be used in order to automatically extract the context. Chapters 3 and 4 focus on the size of this knowledge and provide computational models for its ecient handling. Chapters 5 and 6 deal with the intelligent utilization of such knowledge in the framework if information retrieval.

In the second part of the thesis we move on to a level between concepts and numeric data. Thus, chapter 7 explains how we can use high level linguistic information in order to handle uncertain low level numerical data. Focus is both on the uncertainty within the low level data and on the exibility required in order for the high level information to provide for an adequate description of the real world.

In the third and last part of the thesis we work solely with numerical data. Chapters 8 and 9 deal with the automated analysis of data for the generation of neural models that are able to map the structure of the data, while chapter 10 moves on to the processing of these models in order to automatically extract higher level information from the available numerical data.

Chapter 11 summarizes conclusions drawn from this thesis and refers to directions of possible further work that come out of this work.

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1 known citations

1. P. Tzouveli, Multimedia content analysis and users� categorization in learning environment, PhD Thesis, National Technical University of Athens, School of Electrical and Computer Engineering, July 2009