Social, Group and Individual Mind extracted from Rule Bases of Multiple Agents
This paper shows possibility of extraction Social,
Group and Individual Mind from Multiple Agents Rule Bases. Types
those Rule bases are selected as two fuzzy systems, namely
Mambdani and Takagi-Sugeno fuzzy system. Their rule bases are
describing (modeling) agent behavior. Modifying of agent behavior
in the time varying environment will be provided by learning fuzzyneural
networks and optimization of their parameters with using
genetic algorithms in development system FUZNET. Finally,
extraction Social, Group and Individual Mind from Multiple Agents
Rule Bases are provided by Cognitive analysis and Matching
criterion.
[1] Takagi,T., Sugeno,M.: Fuzzy Identification of Systems and Its
Application to Modelling and Control, IEEE Trans., MAC, 15, No.1,
116-132.
[2] Pokorny.M., ─îermak P.: A Non-linear Regression Analyze Through
Neuro-fuzzy Modeling, The 3rd Czech-Japan Seminar on Data Analysis
and Decision Making under Uncertainty, Osaka, Japan, 30-31 October,
2000.
[3] Cermak,P., Pokorny,M.: An Improvement of Non-Linear Neuro-Fuzzy
Model Properties , Neural Network World, ICS AV CR, Praha, 2001,
ISSN 1210-0552, pp. 503-523.
[4] Goldberg,D.E.: Genetic Algorithms in Search, Optimization, and
Machine Learning, Addison-Wesley Publ. Comp., INC., 1989, ISBN 0-
201-15767-5
[5] Roupec, J.: Genetic optimization algoritms in fuzzy regulators,
dissertation thesis, Brno, 1999.
[6] Cermak,P., Chmiel,P.: Parameters optimization of fuzzy-neural dynamic
models, 23rd International Conference of the North American Fuzzy
Information Processing Society June 27-30, 2004 Banff, AB, Canada,
ISBN 0-7803-8377-X, pp. 762-767.
[7] Cermak,P.: Online Learning of Neural Takagi-Sugeno Fuzzy Model,
24rd International Conference of the North American Fuzzy Information
Processing Society June 22-25, 2005 Ann Arbor, Michigan,USA, ISBN
0-7803-9188-8, IEEE Catalog No. 05TH8815C.
[1] Takagi,T., Sugeno,M.: Fuzzy Identification of Systems and Its
Application to Modelling and Control, IEEE Trans., MAC, 15, No.1,
116-132.
[2] Pokorny.M., ─îermak P.: A Non-linear Regression Analyze Through
Neuro-fuzzy Modeling, The 3rd Czech-Japan Seminar on Data Analysis
and Decision Making under Uncertainty, Osaka, Japan, 30-31 October,
2000.
[3] Cermak,P., Pokorny,M.: An Improvement of Non-Linear Neuro-Fuzzy
Model Properties , Neural Network World, ICS AV CR, Praha, 2001,
ISSN 1210-0552, pp. 503-523.
[4] Goldberg,D.E.: Genetic Algorithms in Search, Optimization, and
Machine Learning, Addison-Wesley Publ. Comp., INC., 1989, ISBN 0-
201-15767-5
[5] Roupec, J.: Genetic optimization algoritms in fuzzy regulators,
dissertation thesis, Brno, 1999.
[6] Cermak,P., Chmiel,P.: Parameters optimization of fuzzy-neural dynamic
models, 23rd International Conference of the North American Fuzzy
Information Processing Society June 27-30, 2004 Banff, AB, Canada,
ISBN 0-7803-8377-X, pp. 762-767.
[7] Cermak,P.: Online Learning of Neural Takagi-Sugeno Fuzzy Model,
24rd International Conference of the North American Fuzzy Information
Processing Society June 22-25, 2005 Ann Arbor, Michigan,USA, ISBN
0-7803-9188-8, IEEE Catalog No. 05TH8815C.
@article{"International Journal of Information, Control and Computer Sciences:61834", author = "P. Cermak", title = "Social, Group and Individual Mind extracted from Rule Bases of Multiple Agents", abstract = "This paper shows possibility of extraction Social,
Group and Individual Mind from Multiple Agents Rule Bases. Types
those Rule bases are selected as two fuzzy systems, namely
Mambdani and Takagi-Sugeno fuzzy system. Their rule bases are
describing (modeling) agent behavior. Modifying of agent behavior
in the time varying environment will be provided by learning fuzzyneural
networks and optimization of their parameters with using
genetic algorithms in development system FUZNET. Finally,
extraction Social, Group and Individual Mind from Multiple Agents
Rule Bases are provided by Cognitive analysis and Matching
criterion.", keywords = "Mind, Multi-agent system, Cognitive analysis, Fuzzy
system, Neural network, Genetic algorithm, Rule base.", volume = "2", number = "3", pages = "917-5", }