Using a multi-agent system and artificial intelligence for monitoring and improving the cloud performance and security

2018 ◽  
Vol 86 ◽  
pp. 1106-1117 ◽  
Author(s):  
Daniel Grzonka ◽  
Agnieszka Jakóbik ◽  
Joanna Kołodziej ◽  
Sabri Pllana
Keyword(s):  
Artificial Intelligence ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

Determination of Factors Affecting Safety Stress in Machine Design by Using Artificial Intelligence Technologies

2008 ◽  
Vol 144 ◽  
pp. 232-237
Author(s):  
Durmus Karayel ◽  
Sinan Serdar Ozkan ◽  
Fahri Vatansever
Keyword(s):  
Artificial Intelligence ◽  
Information Technologies ◽  
Intelligent System ◽  
System Model ◽  
Multi Agent System ◽  
Safety Factors ◽  
Agent System ◽  
Factors Affecting ◽  
Classical Design ◽  
Multi Agent

In this study, an intelligent system model that can evaluate experimental material properties and safety factors is developed. The model contains Artificial Intelligence Technologies such as Artificial Neural Network (ANN) and Fuzzy Logic. It consists of sub modules into interaction. Also, the model can obtain more precision values than interpolation techniques used to classical design. The study contributes to define safety factors, design criterions and safety stress according to a new approach based on information technologies. So, this study can be seen as one of the sub modules of Intelligence Multi Agent System and it can be integrated with Multi Agent System Model for design. Also, it can be used for classical design studies so that results can be quickly obtained. It is expected that this approach will be widely used by designers.

scholarly journals AiD-EM: Adaptive Decision Support for Electricity Markets Negotiations

2019 ◽  
Author(s):  
Tiago Pinto ◽  
Zita Vale
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Electricity Markets ◽  
Evolutionary Computing ◽  
Multi Agent System ◽  
Agent System ◽  
Agent Based ◽  
Multi Agent ◽  
Competitive Electricity Markets

This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computing, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations. AiD-EM demonstration is enabled by its connection to MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Summary on Research of Agent and Multi-Agent System

2012 ◽  
Vol 542-543 ◽  
pp. 1380-1383 ◽  
Author(s):  
You Jie Ma ◽  
Fan Ting Kong ◽  
Xue Song Zhou
Keyword(s):  
Artificial Intelligence ◽  
Development Trend ◽  
Distributed Artificial Intelligence ◽  
Multi Agent System ◽  
Coordination Problem ◽  
Agent System ◽  
Artificial Intelligence Research ◽  
Multi Agent ◽  
The Development Trend ◽  
Important Branch

Distributed artificial intelligence is an important branch of the artificial intelligence， Agent and Multi-agent system are important aspects of distributed artificial intelligence research. This paper mainly introduce some research and exiting problems about Agent and Multi-agent system in recent years, including the Agent concept, characteristics and structure, Multi-agent system concept, structure and the coordination problem. Finally, I look forward to the development trend of Agent and Multi-agent system.

scholarly journals Design and implementation of the multi-agent system in education

2021 ◽  
Vol 13 (4) ◽  
pp. 775-793
Author(s):  
Oussama Hamal ◽  
Nour-Eddine El Faddouli ◽  
Moulay Hachem Alaoui Harouni
Keyword(s):  
Artificial Intelligence ◽  
Recommendation System ◽  
Educational Data Mining ◽  
Secondary Data ◽  
Primary Data ◽  
Multi Agent System ◽  
Agent System ◽  
Learning And Teaching ◽  
Wide Range ◽  
Multi Agent

Nowadays, AI is a real springboard for finding solutions to optimize and improve learning and teaching processes. This issue has been a focus of humanity for millennia, and very significant advances have been made in this quest. This article aims to address the issue of optimizing and improving learning and teaching processes through AI (Artificial Intelligence), considering crossroads of research fields AIED (Artificial Intelligence in Education), EDM (Educational Data Mining) and LA (Learning Analytic). The research made use of secondary data collected from previous research on the topic and primary data was collected using a case study. A comparative analysis was conducted and based on this opportunity, we propose a multi-agent system based on AI techniques, which is capable of performing broader analyses of learning and teaching processes. The research also implemented a prototype of EMAS. Through this system, teachers and learners will be able to access a wide range of relevant and reliable information about learning and teaching processes.   Key words: AIED, EDM, EMAS, LA, recommendation system, education dropping out, emotion detection

Study and Discussion on Related Technologies of RoboCup Robots

2014 ◽  
Vol 598 ◽  
pp. 627-630
Author(s):  
Di Zhao ◽  
Yu Hong Du ◽  
Bao Lin Wu ◽  
Chen Wu ◽  
Meng Li
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Control ◽  
Kalman Filtering ◽  
Control Algorithms ◽  
Multi Agent System ◽  
World Cup ◽  
Agent System ◽  
Experimental Platform ◽  
Multi Agent ◽  
Agent Structure

As a good experimental platform for multi-agent system, Robot Soccer World Cup (RoboCup) has become a hot topic of artificial intelligence. This paper reviewed the technical characteristics of related robots in Simulation, Small Size, Middle Size and Humanoid It introduced the intelligent control algorithms such as Agent structure, Kalman filtering, self-positioning of robots and ZMP stability judgment.

scholarly journals A simulation energy management system of a multi-source renewable energy based on multi agent system

2021 ◽  
Vol 10 (1) ◽  
pp. 191
Author(s):  
Aoukach Basma ◽  
Oukarfi Benyounes
Keyword(s):  
Artificial Intelligence ◽  
Supply And Demand ◽  
Storage System ◽  
Multi Agent System ◽  
Agent System ◽  
Super Capacitor ◽  
Multi Agent ◽  
Artificial Intelligence Algorithm ◽  
Validation Tests ◽  
Electronic Switches

<p>The intermittent nature of renewable energies sources makes their control difficult. One of the solutions to overcome this handicap is to promote hybridization (multi-source system). To ensure continuity of service, a storage system must be coupled to the system. To do so, artificial intelligence based models are developed to respond optimally to the dilemma of energy supply and demand. These models allow the management of the energy flow between the sources (photovoltaic, wind, battery, super capacitor, and generator) and the variable loads by controlling electronic switches according to the availability of the sources. The artificial intelligence algorithm used in this study is multi agent system (MAS). The simulation results and validation tests shows the effectivenes of the proposed approach.</p>

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