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S-DLCAM: A Self-Design and Learning Cooperative Agent Model for Adaptive Multi-agent Systems

Wafa Mefteh, Frederic Migeon, Marie-Pierre Gleizes, Faiez Gargouri
2013 2013 Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises  
OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible.  ...  Keywords-Adaptive Multi-Agent Systems; Cooperative Agent; Self-Design and Learning Cooperative Agent Model. I.  ...  S-DLCAM: A Self-Design and Learning Cooperative Agent Model for Adaptive Multi-Agent Systems Wafa M EF T EH * , * * , Frédéric M IGEON * , Marie-Pierre GLEIZES * , and Faiez GARGOU RI * * *University of  ... 
doi:10.1109/wetice.2013.58 dblp:conf/wetice/MeftehMGG13a fatcat:pholshjqj5grrn3x2stlpabc74

Adaptive fuzzy iterative learning control with initial-state learning for coordination control of leader-following multi-agent systems

Junmin Li, Jinsha Li
2014 Fuzzy sets and systems (Print)  
We propose a distributed adaptive fuzzy iterative learning control (ILC) algorithm to deal with coordination control problems in leader-following multi-agent systems in which each follower agent has unknown  ...  A fuzzy learning component is an important learning tool in the protocol, and combined time-domain and iteration-domain adaptive laws are used to tune the controller parameters.  ...  Moreover, to combine the characteristics of ILC and multi-agent systems, we use distributed initial-state learning.  ... 
doi:10.1016/j.fss.2013.10.010 fatcat:dne4g7rulbbzbelfnkitq4roxu

Cooperative Adaptive Learning Control for a Group of Nonholonomic UGVs by Output Feedback [chapter]

Xiaonan Dong, Paolo Stegagno, Chengzhi Yuan, Wei Zeng
2020 Multi Agent Systems - Strategies and Applications  
In addition, any vehicle in the system is able to learn the knowledge of unmodeled dynamics along the union of trajectories experienced by all vehicle agents, such that the learned knowledge can be re-used  ...  The learning-based tracking convergence and consensus learning results, as well as using learned knowledge for tracking experienced trajectories, are shown using the Lyapunov method.  ...  An observer and RBFNN-based adaptive learning control algorithm is developed for a multi-vehicle system, such that each vehicle agent will be able to follow the desired reference trajectory. iii.  ... 
doi:10.5772/intechopen.87038 fatcat:a6k5rnebavazvfvyuoai2humwu

Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning

H. El Fazazi, M. Elgarej, M. Qbadou, K. Mansouri
2021 Engineering, Technology & Applied Science Research  
In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented.  ...  The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students' needs.  ...  .: Design of an Adaptive e-Learning System based on Multi-Agent Approach and … Fig. 4 . 4 Role and task model of agents.  ... 
doi:10.48084/etasr.3905 fatcat:n4vy5awym5cy3hrkj5tk3u4yhu

Improving the adaptability of multi-agent based E-learning systems

Francisco PINTO-SANTOS, Hector SÁNCHEZ SAN BLAS, Manuel SALGADO DE LA IGLESIA, Xuzeng MAO
2018 Advances in Distributed Computing and Artificial Intelligence Journal  
This paper presents a multi-agent-based e-learning system, as an extension to (Al-Tarabily, 2018).  ...  KEYWORD ABSTRACT multi-agent systems; e-learning; clustering E-Learning is a new learning approach that involves the use of electronic technologies to access education outside of a conventional classroom  ...  The results of the test demonstrate that the proposed extension to the system developed by (Al-Tarabily, 2018) is valid.  ... 
doi:10.14201/adcaij20187516 fatcat:sxqsoado2zcjxpk33bpf6fjdte

Online adaptive learning for team strategies in multi-agent systems

Greg Hudas, Kyriakos G Vamvoudakis, Dariusz Mikulski, Frank L Lewis
2010 The Journal of Defence Modeling and Simulation: Applications, Methodology, Technology  
His current research interests include: multi-agent systems, pattern recognition, and machine learning. He is also the University Relations Liaison, National Auto motive Center (NAC) at the U.S.  ...  This learning approach is based on RL techniques. 7,8 A survey on multi-agent RL is presented by Busoniu et al. 6 It is a general method for solving optimal decision problems for general non-linear dynamical  ...  1 --3 3 2 3 1 3 T 1 1 11 T 21 11 1 T 1 T 3 2 T 2 1 3 (51) and the second actor (second player) NN be tuned as  ... 
doi:10.1177/1548512910382002 fatcat:xa4ljml3hzgdvfmoxnbf4owg3m

Adaptive Design of Role Differentiation by Division of Reward Function in Multi-Agent Reinforcement Learning

Tadahiro TANIGUCHI, Kazuma TABUCHI, Tetsuo SAWARAGI
2010 SICE Journal of Control Measurement and System Integration  
., partial observation, credit assignment, and concurrent learning in multi-agent reinforcement learning.  ...  In many conventional approaches, each agent estimates hidden states, e.g., sensor inputs, positions, and policies of other agents, and reduces the uncertainty in the partially-observable Markov decision  ...  Because of the socially dynamic property of a multi-agent system, it is difficult for participating agents to achieve multi-agent reinforcement learning tasks.  ... 
doi:10.9746/jcmsi.3.26 fatcat:lvqmi5cs2jh5xotwekltcv6rbe

Cooperative and Adaptive Optimal Output Regulation of Discrete-Time Multi-Agent Systems Using Reinforcement Learning

Weinan Gao, Yiyang Liu, Adedapo Odekunle, Zhong-Ping Jiang, Yunjun Yu, Pingli Lu
2018 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR)  
This paper proposes an original data-driven intelligent control solution to the cooperative output regulation problem of discrete-time multi-agent systems.  ...  Based on the combination of internal model principle and reinforcement learning, a distributed suboptimal controller is learned realtime via online input-state data collected from system trajectories.  ...  output regulation problems for discrete-time multi-agent systems.  ... 
doi:10.1109/rcar.2018.8621852 dblp:conf/rcar/GaoLOJYL18 fatcat:54juphy4obeinlolykvrbrfw3y

Agent memory and adaptation in multi-agent systems

Kristina Lerman, Aram Galstyan
2003 Proceedings of the second international joint conference on Autonomous agents and multiagent systems - AAMAS '03  
The agents use memory to estimate the global state of the system from individual observations and adjust their actions accordingly.  ...  We describe a general mechanism for adaptation in multiagent systems in which agents modify their behavior based on their memory of past events.  ...  The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of any  ... 
doi:10.1145/860700.860703 fatcat:zn7ntamqsjcc7dad27n6qoah4u

Agent memory and adaptation in multi-agent systems

Kristina Lerman, Aram Galstyan
2003 Proceedings of the second international joint conference on Autonomous agents and multiagent systems - AAMAS '03  
The agents use memory to estimate the global state of the system from individual observations and adjust their actions accordingly.  ...  We describe a general mechanism for adaptation in multiagent systems in which agents modify their behavior based on their memory of past events.  ...  The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of any  ... 
doi:10.1145/860575.860703 dblp:conf/atal/LermanG03 fatcat:ykcwkgih35c2jjcsm3kmcyk3jq

Reasoning About Adaptivity of Agents and Multi-agent Systems

Graeme Smith, J.W. Sanders, Kirsten Winter
2012 2012 IEEE 17th International Conference on Engineering of Complex Computer Systems  
Although adaptivity is a central feature of agents and multi-agent systems (MAS), there is no precise definition of it in the literature. What does it mean for an agent or for a MAS to be adaptive?  ...  How can we reason about and measure the ability of agents and MAS to adapt? In this paper, we provide a formal definition of adaptivity of agents and MAS aimed at addressing these issues.  ...  Multi-agent systems (MAS) exhibit both forms of adaptivity.  ... 
doi:10.1109/iceccs20050.2012.6299229 fatcat:ycskegw3unewhbzeis4j7fjlxu

An adaptative agent architecture for holonic multi-agent systems

Vincent Hilaire, Abder Koukam, Sebastian Rodriguez
2008 ACM Transactions on Autonomous and Adaptive Systems  
Adaptative Agent Architecture for Holonic Multi-Agent Systems · 5 Fig. 1.  ...  An Adaptative Agent Architecture for Holonic Multi-Agent Systems VINCENT HILAIRE ABDER KOUKAM and SEBASTIAN RODRIGUEZ Systems and Transportation laboratory UTBM 90010 Belfort Cedex, FRANCE Self-organized  ... 
doi:10.1145/1342171.1342173 fatcat:k3oetngfubfg7oeudegzltx6oi

Intelligent Collaboration Environment in Multi-Agent System Enabling Software Dynamic Integration and Adaptive Evolving [chapter]

Qingshan Li, Lili Guo, Xiangqian Zhai, Baoye Xue
2011 Multi-Agent Systems - Modeling, Control, Programming, Simulations and Applications  
Through wrapping legacy systems www.intechopen.com Multi-Agent Systems -Modeling, Control, Programming, Simulations and Applications 244 and newly-developed systems into agents which has the characteristics  ...  In this chapter, the agents are applied to system integration to achieve dynamic system integration and adaptive evolving, through packing the sub-systems into the agents and the communication and collaboration  ...  System Enabling Software Dynamic Integration and Adaptive Evolving Multi-Agent Systems -Modeling, Control, Programming, Simulations and Applications  ... 
doi:10.5772/15161 fatcat:4bmyh6syvjcdzilf3jgykx54fm

A Multi-Agent Self-Adaptive Multi-Objective Genetic Algorithm

Shi LianShuan, Wang HuaHui
2015 International Journal of Intelligent Engineering and Systems  
Agent also possesses some knowledge of the environment and can learn itself while evolving, in order to adapt itself to the environment better and enhance its viability.  ...  A new multi-objective genetic algorithm based on Multi-Agent Self-Adaptive Genetic Algorithm(MASAGA) is proposed, in which the evolution parameters are adjusted adaptively in the evolutionary process and  ...  Agent also possesses some knowledge of the environment and can learn itself while evolving, in order to adapt itself to the environment better and enhance its viability.  ... 
doi:10.22266/ijies2015.0630.02 fatcat:un7ewmoqdbgpfgwib6mzqemjde

Optimal adaptive leader-follower consensus of linear multi-agent systems: Known and unknown dynamics

2015 Journal of Artificial Intelligence and Data Mining  
The decoupling of the multi-agent system global error dynamics facilitates the employment of policy iteration and optimal adaptive control techniques to solve the leaderfollower consensus problem under  ...  In this paper, the optimal adaptive leader-follower consensus of linear continuous time multi-agent systems is considered. The error dynamics of each player depends on its neighbors' information.  ...  systems, we wish to achieve synchronization in the multi-agent system simultaneously optimizing some performance specifications on the agents.  ... 
doi:10.5829/idosi.jaidm.2015.03.01.11 fatcat:acjqisj2ujbrjkzzwei4coqr4u
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