Special issue on “Collective Behavior and Control of Multi-Agent Systems”

2008 ◽  
Vol 10 (2) ◽  
pp. 129-130 ◽  
Author(s):  
Xiaoming Hu ◽  
Yiguang Hong ◽  
Linda Bushnell
Keyword(s):  
Collective Behavior ◽  
Multi Agent Systems ◽  
Special Issue ◽  
Agent Systems ◽  
Multi Agent ◽  
And Control

scholarly journals Special issue on learning and control in cooperative multi-agent systems

2015 ◽  
Vol 13 (1) ◽  
pp. 44-44
Author(s):  
Frank L. Lewis ◽  
Zhong-Ping Jiang ◽  
Tengfei Liu
Keyword(s):  
Multi Agent Systems ◽  
Special Issue ◽  
Agent Systems ◽  
Multi Agent ◽  
And Control

Dynamic Generation and Control of Optimally Persistent Formation for Multi-agent Systems

2014 ◽  
Vol 39 (9) ◽  
pp. 1431-1438 ◽  
Author(s):  
Xiao-Yuan LUO ◽  
Shi-Kai SHAO ◽  
Xin-Ping GUAN ◽  
Yuan-Jie ZHAO
Keyword(s):  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Dynamic Generation ◽  
And Control

scholarly journals Impulsive Consensus Tracking of Multiagent Systems with Quantization and Input Delays Using Position-Only Information

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hong Zhou ◽  
Yu-Zhen Qin ◽  
Kuo Feng ◽  
Wen-Shan Hu ◽  
Zhi-Wei Liu
Keyword(s):  
Sampling Period ◽  
Second Order ◽  
Quantization Scheme ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Consensus Tracking ◽  
Sufficient And Necessary Conditions ◽  
Multi Agent ◽  
Input Delays ◽  
And Control

This paper investigates the consensus tracking problem for second-order multi-agent systems without/with input delays. Randomized quantization scheme is considered in the communication channels, and impulsive consensus tracking algorithms using position-only information are proposed for the consensus tracking of multi-agent systems. Based on the algebraic graph theory and stability theory of impulsive systems, sufficient and necessary conditions for consensus tracking are studied. It is found that consensus tracking for second-order multi-agent systems without/with input delays can be achieved by appropriately choosing the sampling period and control gains which are determined by second/third degree polynomials. Simulations are performed to validate the theoretical results.

scholarly journals Reports on the 2018 AAAI Spring Symposium Series

AI Magazine ◽  
2018 ◽  
Vol 39 (4) ◽  
pp. 29-35
Author(s):  
Christopher Amato ◽  
Haitham Bou Ammar ◽  
Elizabeth Churchill ◽  
Erez Karpas ◽  
Takashi Kido ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
Well Being ◽  
Machine Intelligence ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Internet Of Everything ◽  
Symposium Series ◽  
Multi Agent ◽  
And Control

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University’s Department of Computer Science, presented the 2018 Spring Symposium Series, held Monday through Wednesday, March 26–28, 2018, on the campus of Stanford University. The seven symposia held were AI and Society: Ethics, Safety and Trustworthiness in Intelligent Agents; Artificial Intelligence for the Internet of Everything; Beyond Machine Intelligence: Understanding Cognitive Bias and Humanity for Well-Being AI; Data Efficient Reinforcement Learning; The Design of the User Experience for Artificial Intelligence (the UX of AI); Integrated Representation, Reasoning, and Learning in Robotics; Learning, Inference, and Control of Multi-Agent Systems. This report, compiled from organizers of the symposia, summarizes the research of five of the symposia that took place.

Multi-Agent Control System for the Movement of Uniaxial Objects

2015 ◽  
Vol 237 ◽  
pp. 183-188
Author(s):  
Mirosław Mrozek
Keyword(s):  
Control System ◽  
Point Of View ◽  
Multi Agent Systems ◽  
Control Groups ◽  
Agent Systems ◽  
Multi Agent ◽  
Agent Control ◽  
Classical Control ◽  
And Control ◽  
Multi Agents

Multi-agent systems are used mainly in IT solutions and control groups of robots. From the point of view of classical control architectures, they are a kind of distributed systems in which nodes perform advanced algorithms, usually associated with the technology of artificial intelligence, and they can be considered as agents. The article describes the multi-agents control system of objects of uniaxial movements. An example of such a system to control a repository with movable racks with electric motors is presented. Each rack acts as an agent through the implemented control of the resources of embedded microcontrollers. Such a system provides high quality control, guaranteeing long-lasting, trouble-free operation while maintaining the safety of both service and stored items.

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