Resource Scheduler Based on Multi-agent Model and Intelligent Control System for OpenStack

2014 ◽  
pp. 556-566 ◽  
Author(s):  
Alexey Lukashin ◽  
Anton Lukashin
Keyword(s):  
Control System ◽  
Intelligent Control ◽  
Agent Model ◽  
Intelligent Control System ◽  
Multi Agent ◽  
Resource Scheduler

Construction Research of Indoor Environment Intelligent Control System Based on MAS

2011 ◽  
Vol 71-78 ◽  
pp. 494-497 ◽  
Author(s):  
Wei Zheng ◽  
Yi Fei Chen
Keyword(s):  
Control System ◽  
Intelligent Control ◽  
Indoor Environment ◽  
Comfort Index ◽  
Intelligent Control System ◽  
Task Distribution ◽  
Environment Control ◽  
Cooperation Strategy ◽  
Multi Agent ◽  
Environment Parameters

Strictly speaking, the indoor environment intelligent control system can not only detect the parameters of indoor environment, the state of every equipment, but also adjust indoor environment automatically in order to achieve the most comfortable indoor environment under condition of saving energy. Based on thermal comfort index PMV, we have constructed the MAS (Multi-Agent System) for indoor environment control with Zigbee network technology after the deeply research on the decoupling of the environment parameters. In this paper, main researching contents have been discussed like the cooperation strategy in the MAS, proposed a cooperation strategy based on the task distribution, which decreased the communication and enhanced problem solving efficiency.

A Layered, Multi-Agent System for Intelligent Control

1992 ◽  
Vol 4 (2) ◽  
pp. 152-158
Author(s):  
Takuya Ishioka ◽  
◽  
Morikazu Takegaki
Keyword(s):  
Real Time ◽  
Intelligent Control ◽  
Time Constraints ◽  
Multi Agent System ◽  
Real Time Control ◽  
Agent Model ◽  
Intelligent Control System ◽  
Time Control ◽  
Rule Set ◽  
Multi Agent

We propose a layered, multi-agent model as a method for establishing an intelligent control system. The model handles each task, which constitutes control processing, as a group of agents operating autonomously, and organizes the agents in a layered structure from the viewpoint of real-time constraints. This enables real-time control processing and knowledge information processing simultaneously. We will describe a method for building the layered multiagent model using a concurrent rule set. The concurrent rule set can be processed by extending the expert shell and can realize the layered agent model in various computer environments through the use of the shell.

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