Abstract
The paper presents a method of a fuzzy knowledge processing in a massively parallel way. The key concept is Running Agent (RAT)—a mobile entity carrying a piece of processable information. A Working Memory inhabited by a society of RATs may be implemented in two versions: (i) recurrent neural network in which RATs are represented by navigating patterns, or (ii) a test tube containing a suspension where RATs are molecules of defined structures. The RATs are continuously indoctrinated in such a way, that for a given period of time n1 RATs are made to have an opinion “A”, while n2 RATs are made to have an opinion“not A”, where n1/(n1+n2) equals the assumed degree of membership of a given percept in the fuzzy set A. Some other RATs are made to adhere logical rules related to the A. During a “debate” in the society, concluding opinions are produced. Hence, one may observe a fizzy growth of populations of RATs carrying several contradictory statements. When a poll indicates a domination of one of the populations in the Working Memory, it may be considered as finding of a final solution. The discussed method of inferencing may play in knowledge processing similar role as the Monte Carlo method plays in digital integration. Some results of experiments with a simulation model of a Fizzy-Fuzzy system are also discussed.
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© 1996 Springer-Verlag Berlin Heidelberg
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Buller, A. (1996). Fizzy-Fuzzy inferencing. In: Furuhashi, T., Uchikawa, Y. (eds) Fuzzy Logic, Neural Networks, and Evolutionary Computation. WWW 1995. Lecture Notes in Computer Science, vol 1152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61988-7_21
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DOI: https://doi.org/10.1007/3-540-61988-7_21
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