Handling Fuzziness in Knowledge-Based Systems

Abstract

In this Ch., as well as in the following, we will study a unified approach for handling and processing sources of uncertainty in knowledge-based systems. This goal is achieved in the framework of fuzzy relation equations. We point out how the mechanisms of the theory developed in the previous Chs. of this book can be treated as a convenient platform for construction of knowledge-based systems. More precisely, it will be indicated how fuzzy equations contribute to each of the conceptual levels recognized in the construction of these systems (viz. knowledge representation, meta-knowledge, inference techniques, etc.) as well as how they are directly used in formation of the particular elements of the problem-oriented expert systems. It is assumed that the reader has a certain background concerning Knowledge Engineering and Artificial Intelligence, at least on fundamentals of architecture of knowledge-based systems. It is also expected that he is familiar with some of the well-known expert systems, especially those broadly documented in literature (e.g. PROSPECTOR, MYCIN) and mechanisms involved there which are capable of coping with uncertainty, no matter how it has been introduced. This Ch. must be viewed as a concise prerequisite for the successive Chs. and it indicates many problems occurring in knowledge engineering when factors of uncertainty have to be processed.