Abstract
One of the most interesting aspects of Expert Systems research is to gain some insights about human problem solving strategies by trying to emulate them in programs. Experts in a domain are better than novices in performing problem solving tasks. This is due to their greater experience in solving problems that provides them with better strategies. Such strategies are knowledge about how to use the knowledge they have in their domain of expertise. This kind of knowledge is called metaknowledge and is represented by means of meta-rules in the MILORD system for diagnostic reasoning. Diagnostic reasoning heavily involves metaknowledge to focuss attention on the most plausible hypotheses or goals in a given situation and to control the inference process. Furthermore, uncertainty also plays an important role at the control level, for example, decisions are taken depending on the uncertainty of the facts supporting them.
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© 1992 Springer Science+Business Media New York
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de Mántaras, R.L., Sierra, C., Agustí, J. (1992). The Representation and Use of Uncertainty and Metaknowledge in Milord. In: Yager, R.R., Zadeh, L.A. (eds) An Introduction to Fuzzy Logic Applications in Intelligent Systems. The Springer International Series in Engineering and Computer Science, vol 165. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3640-6_12
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DOI: https://doi.org/10.1007/978-1-4615-3640-6_12
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