Survey of the State and Future Trends of Intelligent Systems

This paper presents an attempt to formalize objective macro model of the field of artificial intelligence (AI). We show that creation of this model is justified, and with the aid of information from World Wide Web it is possible now. With this aim in view, we propose a research method. To obtain a macro model of the artificial intelligence field, we made a survey of research groups in the world, including companies, applications, organizations as well as a general assessment of the state of Al technology. The survey is intended to show some benefits. Intelligent systems are becoming very useful and are starting to achieve many of the projected past promises. Important documents on future trends and roles of intelligent systems have been recently published, as well as interesting surveys in the field. We have assessed several methodologies of research of the state of the art in this field and identified their promises and limitations. Considering the present state of Al technology, research projects, important documents and trends in traditional information technologies, we have made a preliminary model of intelligent systems and their future surveys. In the era of second generation knowledge-based systems and growing complexity of the Al field, we believe that it is necessary to include macro model of Al in all scientific researches and application projects

Fuzzy Membership Function Initial Values: Comparing Initialization Methods That Expedite Convergence

Fuzzy attributes are used to quantify imprecise data that model real world objects. To effectively use fuzzy attributes, a fuzzy membership function must be defined to provide the boundaries for the fuzzy data. The initialization of these membership function values should allow the data to converge to a stable membership value in the shortest time possible. The paper compares three initialization methods, Random, Midpoint and Random Proportional, to determine which method optimizes convergence. The comparison experiments suggest the use of the Random Proportional method

What does fuzzy logic bring to AI?

International audienceThe term “fuzzy logic” often refers to a particular control-engineering methodology that exploits a numerical representation of commensense control rules in order to synthesize, via interpolation, a control law. This approach has many features in common with neural networks. It is currently concerned mainly with the efficient encoding and approximation of numerical functions and has less and less relationship to knowledge representation issues. This is, however, a very narrow view of fuzzy logic that has little to do with AI. Scanning the fuzzy set literature, one realizes that fuzzy logic may also refer to two other topics: multiplevalued Iogics and approximate reasoning. Although the multiple-valued logic stream is very mathematically oriented, the notion of approximate reasoning as imagined by Zadeh is much more closely related to the program of AI research: he wrote in 1979 that “the theory of approximate reasoning is concerned with the deduction of possibly imprecise conclusions from a set of imprecise premises.” In the following, we use the term “fuzzy logic” to mean any kind of fuzzy set-based method intended to be used in reasoning systems. Fuzzy logic is 30 years old and has a long-term misunderstanding with AI. As a consequence, fuzzy logic methods have not been considered to belong to mainstream AI tools until now, although an important part of fuzzy logic research concentrates on issues in approximate reasoning and reasoning under uncertainty. Some reasons for this situation may be found in the antagonism which existed for a long time between purely symbolic methods advocated by AI and the numerically oriented approaches that were involved in fuzzy rule-based systems. Besides, fuzzy sets were a new emerging approach not yet firmly settled, but apparently challenging the monopoly of probability theory on being the unique proper framework for handling uncertainty. In spite of the fact that fuzzy sets have received more recognition recently, there is still a lack of appreciation by AI researchers of what fuzzy logic really is, as, for instance, recently exemplified by Elkan [ 1994]

What does fuzzy logic bring to AI?

International audienceThe term “fuzzy logic” often refers to a particular control-engineering methodology that exploits a numerical representation of commensense control rules in order to synthesize, via interpolation, a control law. This approach has many features in common with neural networks. It is currently concerned mainly with the efficient encoding and approximation of numerical functions and has less and less relationship to knowledge representation issues. This is, however, a very narrow view of fuzzy logic that has little to do with AI. Scanning the fuzzy set literature, one realizes that fuzzy logic may also refer to two other topics: multiplevalued Iogics and approximate reasoning. Although the multiple-valued logic stream is very mathematically oriented, the notion of approximate reasoning as imagined by Zadeh is much more closely related to the program of AI research: he wrote in 1979 that “the theory of approximate reasoning is concerned with the deduction of possibly imprecise conclusions from a set of imprecise premises.” In the following, we use the term “fuzzy logic” to mean any kind of fuzzy set-based method intended to be used in reasoning systems. Fuzzy logic is 30 years old and has a long-term misunderstanding with AI. As a consequence, fuzzy logic methods have not been considered to belong to mainstream AI tools until now, although an important part of fuzzy logic research concentrates on issues in approximate reasoning and reasoning under uncertainty. Some reasons for this situation may be found in the antagonism which existed for a long time between purely symbolic methods advocated by AI and the numerically oriented approaches that were involved in fuzzy rule-based systems. Besides, fuzzy sets were a new emerging approach not yet firmly settled, but apparently challenging the monopoly of probability theory on being the unique proper framework for handling uncertainty. In spite of the fact that fuzzy sets have received more recognition recently, there is still a lack of appreciation by AI researchers of what fuzzy logic really is, as, for instance, recently exemplified by Elkan [ 1994]