Exploring Flows in the Intelligent Agent Grid Environment

2005 ◽  
pp. 13-24
Author(s):  
Zhuge Hai
Keyword(s):  
Intelligent Agent ◽  
Grid Environment

User Authentication Schemes of Remote Access Devices for Smart-Home Grid Environment

2015 ◽  
Vol 16 (1) ◽  
pp. 65-82
Author(s):  
김창희 ◽  
HONG YOUN SIK ◽  
최성욱
Keyword(s):  
Smart Home ◽  
User Authentication ◽  
Remote Access ◽  
Grid Environment ◽  
Authentication Schemes

THE USE OF DYNAMIC VIRTUAL IMAGES IN A GRID ENVIRONMENT WITH REPLICATION SUPPORT

2017 ◽  
pp. 88-97 ◽  
Author(s):  
Volodymyr Kazymyr ◽  
◽  
Olga Prila ◽  
Mykola Kryshchenko ◽  
◽  
...  
Keyword(s):  
Grid Environment ◽  
Virtual Images

Efficient Scheduling Algorithm for the Exchange of Data in Grid Environment

2009 ◽  
Vol 3 (3) ◽  
pp. 35-42
Author(s):  
Sumathi P ◽  
Punithavalli M
Keyword(s):  
Scheduling Algorithm ◽  
Grid Environment

G-R_TRBAC access control model in grid environment

2009 ◽  
Vol 28 (12) ◽  
pp. 3214-3216
Author(s):  
Yi DING ◽  
Yong FANG ◽  
An-min ZHOU ◽  
Jiao ZENG ◽  
Yu FAN
Keyword(s):  
Access Control ◽  
Control Model ◽  
Grid Environment ◽  
Access Control Model

Information query based on semantic association in grid environment

2009 ◽  
Vol 29 (6) ◽  
pp. 1517-1519
Author(s):  
Xiao-juan ZHANG ◽  
Hua LI
Keyword(s):  
Semantic Association ◽  
Grid Environment

Distributed Generation Units as Ancillary Services Providers in a Pre Smart Grid Environment

2015 ◽  
Vol 9 (1) ◽  
pp. 432-444
Author(s):  
Clainer Bravin Donadel ◽  
Jussara Farias Fardin ◽  
Lucas Frizera Encarnação
Keyword(s):  
Smart Grid ◽  
Distributed Generation ◽  
Ancillary Services ◽  
Grid Environment

scholarly journals Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 681
Author(s):  
László Barna Iantovics
Keyword(s):  
Problem Solving ◽  
Scientific Literature ◽  
Real Life ◽  
Intelligent Agent ◽  
Difficult Problem ◽  
Black Box ◽  
Machine Intelligence ◽  
Agent Based ◽  
Np Hard Problem ◽  
Box Method

Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of comparing the intelligence of only two CMASs at an application. In this paper, we propose a generalization of that metric called MetrIntPairII. MetrIntPairII is based on pairwise problem-solving intelligence comparisons (for the same problem, the problem-solving intelligence of the studied CMASs is evaluated experimentally in pairs). The pairwise intelligence comparison is proposed to decrease the necessary number of experimental intelligence measurements. MetrIntPairII has the same properties as MetrIntPair, with the main advantage that it can be applied to any number of CMASs conserving the accuracy of the comparison, while it exhibits enhanced robustness. An important property of the proposed metric is the universality, as it can be applied as a black-box method to intelligent agent-based systems (IABSs) generally, not depending on the aspect of IABS architecture. To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several CMASs composed of agents specialized in solving an NP-hard problem.

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