Abstract
In this paper we discuss possible future directions of research for soft computing in the context of artificial intelligence and knowledge engineering. Fundamental issues are presented with basic ideas emphasised rather than detailed accounts of algorithms and procedures.
The use of fuzzy sets to machine learning, computer intelligence and creativity are discussed in relation to the central problems of creating knowledge from data, pattern recognition, making summaries, user modelling for computer/human interfaces, co-operative learning, fuzzy inheritance for associative reasoning.
A voting model semantics for fuzzy sets is used to develop ideas for a mass assignment theory which provides the means of moving from the case of crisp sets to that of fuzzy sets. The use of fuzzy sets in this way will provide better interpolation, greater knowledge compression, and less dependence on the effects of noisy data than if only crisp sets were used. We will see how easy and useful it is to use successful inference methods such as decision trees, probabilistic fuzzy logic type rules, Bayesian nets with attributes taking fuzzy values rather than crisp values.
The mass assignment theory provides a unified approach to handling both probabilistic uncertainty and fuzzy vagueness.
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Baldwin, J.F. (2000). Future Directions for Soft Computing. In: Azvine, B., Nauck, D.D., Azarmi, N. (eds) Intelligent Systems and Soft Computing. Lecture Notes in Computer Science(), vol 1804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720181_3
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DOI: https://doi.org/10.1007/10720181_3
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