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Artificial Intelligence and Soft Computing - ICAISC 2004: by Danilo Ardagna, Chiara Francalanci, Vincenzo Piuri, Fabio

By Danilo Ardagna, Chiara Francalanci, Vincenzo Piuri, Fabio Scotti (auth.), Leszek Rutkowski, Jörg H. Siekmann, Ryszard Tadeusiewicz, Lotfi A. Zadeh (eds.)

This e-book constitutes the refereed lawsuits of the seventh overseas convention on man made Intelligence and smooth Computing, ICAISC 2004, held in Zakopane, Poland in June 2004.

The 172 revised contributed papers offered including 17 invited papers have been conscientiously reviewed and chosen from 250 submissions. The papers are geared up in topical sections on neural networks, fuzzy platforms, evolutionary algorithms, tough units, gentle computing in type, snapshot processing, robotics, multiagent structures, difficulties in AI, clever keep watch over, modeling and method identity, scientific purposes, mechanical purposes, and functions in a variety of fields.

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Extra info for Artificial Intelligence and Soft Computing - ICAISC 2004: 7th International Conference, Zakopane, Poland, June 7-11, 2004. Proceedings

Example text

4. , Pirkul, H. 1986. Computer and Database Location in Distributed Computer Systems. IEEE Transaction on Computers. 35(7), 583-590. 5. Jain, H. K. 1987. A comprehensive model for the design of distributed computer systems. IEEE Transactions on software engineering. 13(10), 1092-1104. 6. Dewire D. T. 1997. Second-generation Client/Server Computing. McGraw Hill. 7. Menasce, D. , Alameida, V. A. F. 2000. Scaling for E-business. Technologies, models, performance and capacity planning. Prentice-Hall.

The learning process consists here in step by step knowledge evaluation and updating [1, 5,14,12]. The combination with the approach based on the uncertain variables leads to the learning system based on the current expert’s knowledge. In such a system, at each step of the learning process the expert’s knowledge in the form 26 Z. Bubnicki z z Deterministic algorithm Ψ (z ) ud y Plant G (u, z ) Determinization Dy v (u , z , w) Knowledge of the plant KP f (w) III h( x ; w) II R ( u , y , z; x ) I Fig.

The uncertain variable x is defined by X, the function h(x) = v(x ∼ = x) given by an expert and the following definitions: v(x∈Dx ) = max h(x) for Dx = ∅ x∈Dx and 0 for Dx = ∅, / x ) = 1 − v(x∈Dx ), v(x∈D v(x∈D1 ∨ x∈D2 ) = max v(x∈D1 ), v(x∈D2 ) , v(x∈D1 ∧ x∈D2 ) = min v(x∈D1 ), v(x∈D2 ) 0 for D1 ∩ D2 = ∅ for D1 ∩ D2 = ∅ . The function h(x) will be called a certainty distribution ✷ 20 Z. Bubnicki The definition of the uncertain variable is based on logic L. Then for (1) the properties (2), (3), (4) are satisfied.

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