Handling Fuzziness in Knowledge-Based Systems
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.
Unable to display preview. Download preview PDF.
- P.P. Bonissone and K.S. Decker, Selecting uncertainty calculi and granularity, in: Uncertainty in Artificial Intelligence ( L. Kanal and J. Lemmer, Eds.), North-Holland, New York (1986), pp. 2217–2247.Google Scholar
- A.L. Brown, Modal propositional semantics for reason maintenance system, Proceedings of the 9th Internat. Conference on Artificial Intelligence, Los Angeles, California, 1985.Google Scholar
- P.R. Cohen and M.R. Grinberg, A framework for heuristics reasoning about uncertainty, Proceedings of the 8th Internat. Joint Conference on Artificial Intelligence, Karlsruhe, West Germany (1983), 355–357.Google Scholar
- J. Fox, D.C. Barber and K.D. Bardhar, Alternative to Bayes? A quantitative comparison with rule-based diagnostic inference, Method of Information in Medicine 19 (1980), 210–215.Google Scholar
- M.R. Genesereth, An Overview of MRS for AI Experts, Stanford Heuristic Programming Project, Report n. HPP-82–27, Dept. of Computer Science, Stanford Univ., 1982.Google Scholar
- M.L. Ginsberg, Non-monotonic reasoning using Dempster’s rule, Proceedings of the National Conference on Artificial Intelligence, Austin, Texas, 1984, 126–129.Google Scholar
- M.L. Ginsberg, Implementing Probabilistic Reasoning, Stanford Heuristic Programming Project, Report n. HPP-84–31, Dept. of Computer Science, Stanford Univ., 1984.Google Scholar
- D.A. McAllester, An Outlook on Truth Maintenance, MIT Artificial Intelligence Laboratory, Cambridge, Mass., 1980.Google Scholar
- D.A. Waterman, A guide to Expert Systems, Addison-Wesley, Reading, Mass., 1986.Google Scholar
- L.A. Zadeh, Fuzzy sets and information granularity, in: Advances in Fuzzy Set Theory and Applications ( M.M. Gupta, R.K. Ragade and R.R. Yager, Eds.), North-Holland, Amsterdam (1979), pp. 3–18.Google Scholar
- A C Zimmer, The estimation of subjective probabilities via categorical judgements of uncertainty, in: Uncertainty in Artificial Intelligence ( L. Kanal and J. Lemmer, Eds.), North-Holland, New York (1986), pp. 249–258.Google Scholar