Mazilescu, Vasile (2010): The Semantic Web Paradigm for a Real-Time Agent Control (Part II).
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Abstract
This paper is the second part of The Semantic Web Paradigm for a Real-time 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 |
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Original Title: | The Semantic Web Paradigm for a Real-Time Agent Control (Part II) |
English Title: | The Semantic Web Paradigm for a Real-Time 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: | 02 Oct 2019 04:30 |
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 10-13 May, p. 967-977; 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 0-262-23203-0 Zadeh, L.A. (1983), The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets and Systems, n. 11, p. 199-227 |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/20760 |