Mazilescu, Vasile (2010): The Semantic Web Paradigm for a RealTime Agent Control (Part II).

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Abstract
This paper is the second part of The Semantic Web Paradigm for a Realtime Agent Control, and the goal is to present the predictability of a multiagent system used in a learning process for a control problem (MASLCP).
Item Type:  MPRA Paper 

Original Title:  The Semantic Web Paradigm for a RealTime Agent Control (Part II) 
English Title:  The Semantic Web Paradigm for a RealTime Agent Control (Part II) 
Language:  English 
Keywords:  learning process, fuzzy control, agent predictability 
Subjects:  C  Mathematical and Quantitative Methods > C6  Mathematical Methods; Programming Models; Mathematical and Simulation Modeling > C63  Computational Techniques; Simulation Modeling C  Mathematical and Quantitative Methods > C8  Data Collection and Data Estimation Methodology; Computer Programs > C88  Other Computer Software 
Item ID:  20760 
Depositing User:  Vasile Mazilescu 
Date Deposited:  17. Feb 2010 23:47 
Last Modified:  19. Feb 2013 20:04 
References:  Antsaklis P.J., Lemmon M., Stiver J.A. (1996), Learning to Be Autonomous – Intelligent Supervisory Control, in “Intelligent Control Systems – Theory and Applications” by Gupta M.M., Sinha N.K., IEEE Press; Farrell J., Baker W. (1996), Learning Control Systems – Motivation and Implementation, in Intelligent Control Systems. Theory and Applications by Gupta M.M., Sinha N.K., IEEE Press; Mazilescu V. (2001), The Management of Fuzzy Knowledge in Planning Systems, The Fifth International Symposium of Economic Informatics, Bucharest 1013 May, p. 967977; Mazilescu V., Căpriţă D., 2003 – Sistem de învăţare bazat pe un model hibrid de cunoştinţe, Revista de Informatică Economică, Volumul VII, Nr3 , p. 23 – 29 Peng Y., Reggia A. (1990), Abductive Inference Models for Diagnostic Problem Solving, Springer–Verlag; Sandip S., Weiss G. (2001), Learning in Multiagent Systems, Multiagent Systems, ISBN 0262232030 Zadeh, L.A. (1983), The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets and Systems, n. 11, p. 199227 
URI:  http://mpra.ub.unimuenchen.de/id/eprint/20760 