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distributed multiagent system for Time-Varying Quadratic Programming With Application to Target Encirclement of Multirobot system

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2024年第9期54卷 5339-5351页

作者： Liu, Qingshan Yue, Yuanyuan Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Sch Math Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China

In this article, an unified distributed multiagent system is proposed to minimize the time-varying quadratic function under the time-varying coupled equality constraint, which is further applied to target encirclement of the multirobot system. The time-varying quadratic programming problem under consideration is a generalized version of some found in the literature. The proposed unified distributed multiagent system is effective in both of the following cases of time-varying quadratic programming: one with the nonidentical time-invariant Hessian and constraint matrices, and the other with the identical time-varying Hessian and constraint matrices. The convergence of the time-varying states can be guaranteed if the graph of the communication network is connected and undirected, subject to certain mild conditions. Additionally, simulations show that the suggested approach is effective in resolving the target encirclement problem of the multirobot systems.

关键词： distributed multiagent system multirobot system target encirclement time-varying quadratic programming

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Safe Adaptive Policy Transfer Reinforcement Learning for distributed multiagent Control

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2025年第1期36卷 1939-1946页

作者： Du, Bin Xie, Wei Li, Yang Yang, Qisong Zhang, Weidong Negenborn, Rudy R. Pang, Yusong Chen, Hongtian Northwestern Polytech Univ Ocean Inst Taicang 215400 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Xian Inst High Tech Xian 710071 Peoples R China Delft Univ Technol Dept Maritime & Transport Technol NL-2628 CD Delft Netherlands

multiagent reinforcement learning (RL) training is usually difficult and time-consuming due to mutual interference among agents. Safety concerns make an already difficult training process even harder. This study proposes a safe adaptive policy transfer RL approach for multiagent cooperative control. Specifically, a pioneer and follower off-policy policy transfer learning (PFOPT) method is presented to help follower agents acquire knowledge and experience from a single well-trained pioneer agent. Notably, the designed approach can transfer both the policy representation and sample experience provided by the pioneer policy in the off-policy learning. More importantly, the proposed method can adaptively adjust the learning weight of prior experience and exploration according to the Wasserstein distance between the policy probability distributions of the pioneer and the follower. Case studies show that the distributed agents trained by the proposed method can complete a collaborative task and acquire the maximum rewards while minimizing the violation of constraints. Moreover, the proposed method can also achieve satisfactory performance in terms of learning speed and success rate.

关键词： Adaptive weight update distributed multiagent system policy transfer safe reinforcement learning (RL) transfer learning

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A platform for P2P agent-based collaborative applications

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SOFTWARE-PRACTICE & EXPERIENCE 2019年第3期49卷 549-558页

作者： Briola, Daniela Micucci, Daniela Mariani, Leonardo Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy

The operational environment can be a valuable source of information about the behavior of software applications and their usage context. Although a single instance of an application has limited evidence of the range of the possible behaviors and situations that might be experienced in the field, the collective knowledge composed by the evidence gathered by the many instances of a same application running in several diverse user environments (eg, a browser) might be an invaluable source of information. This information can be exploited by applications able to autonomously analyze how they behave in the field and adjust their behavior accordingly. Augmenting applications with the capability to collaborate and directly share information about their behavior is challenging because it requires the definition of a fully decentralized and dependable networked infrastructure whose nodes are the user machines. The nodes of the infrastructure must be collaborative, to share information, and autonomous, to exploit the available information to change their behavior, for instance, to better accommodate the needs of the users to prevent known problems. This paper describes the initial results that we obtained with the design and the development of an infrastructure that can enable the execution of collaborative scenarios in a fully decentralized way. Our idea is to combine the agent-based paradigm, which is well suited to design collaborative and autonomous nodes, and the peer-to-peer paradigm, which is well suited to design distributed and dynamic network infrastructures. To demonstrate our idea, we augmented the popular JADE agent-based platform with a software layer that supports both the creation of a fully decentralized peer-to-peer network of JADE platforms and the execution of services within that network, thus enabling JADE multiagent systems (MASs) to behave as peer-to-peer networks. The resulting platform can be used to study the design of collaborative applications run

关键词： agent-based paradigm diagnosis distributed multiagent system JADE MAS peer-to-peer systems

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DIMIX: DIMINISHING MIXING FOR SLOPPY AGENTS*

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SIAM JOURNAL ON OPTIMIZATION 2023年第2期33卷 978-1005页

作者： Reisizadeh, Hadi Touri, Behrouz Mohajer, Soheil Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55414 USA Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

We study nonconvex distributed optimization problems where a set of agents collaboratively solve a separable optimization problem that is distributed over a time-varying network. The existing methods to solve these problems rely on (at most) one-time-scale algorithms, where each agent performs a diminishing or constant step-size gradient descent at the average estimate of the agents in the network. However, if possible at all, exchanging exact information, which is required to evaluate these average estimates, potentially introduces a massive communication overhead. Therefore, a reasonable practical assumption to be made is that agents only receive a rough approximation of the neighboring agents' information. To address this, we introduce and study a two-time-scale decentralized algorithm with a broad class of lossy information sharing methods (which includes noisy, quantized, and/or compressed information sharing) over time-varying networks. In our method, one time-scale suppresses the (imperfect) incoming information from the neighboring agents, and one time-scale operates on local cost functions' gradients. We show that with a proper choices for the step-sizes' parameters, the algorithm achieves a convergence rate of \scrO (T - 1/3+\epsilon ) for nonconvex distributed optimization problems over time-varying networks for any \epsilon > 0.

关键词： Key words nonconvex optimization time-varying graphs distributed multiagent system distributed optimization gradient descent algorithms

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distributed Optimization Over Time-Varying Graphs With Imperfect Sharing of Information

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第7期68卷 4420-4427页

作者： Reisizadeh, Hadi Touri, Behrouz Mohajer, Soheil Univ Minnesota Minneapolis MN 55455 USA Univ Calif San Diego La Jolla 92093 CA USA

We study strongly convex distributed optimization problems where a set of agents are interested in solving a separable optimization problem collaboratively. In this article, we propose and study a two-time-scale decentralized gradient descent algorithm for a broad class of lossy sharing of information over time-varying graphs. One time-scale fades out the (lossy) incoming information from neighboring agents, and one time-scale regulates the local loss functions' gradients. We show that assuming a proper choice of step-size sequences, certain connectivity conditions, and bounded gradients along the trajectory of the dynamics, the agents' estimates converge to the optimal solution with the rate of O(T-1/2). We also provide novel tools to study distributed optimization with diminishing averaging weights over time-varying graphs.

关键词： Convex optimization distributed multiagent system distributed optimization gradient descent algorithms time-varying graphs

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Behavior modeling and control of 300 mm fab intrabays using distributed agent oriented Petri net

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IEEE TRANSACTIONS ON systemS MAN AND CYBERNETICS PART A-systemS AND HUMANS 2003年第5期33卷 641-648页

作者： Kuo, CH Wang, CH Huang, KW Chang Gung Univ Dept Mech Engn Distributed Control & Applicat Lab Kaohsiung Taiwan

In a 300 mm integrated circuit (IC) foundry fab, the intrabay automation is more important than a 200 min ones In general, the intrabay automation systems include the automated controls of the process tools, overhead hoist transports (OHTs), stockers, and rail shortcuts. These control systems are operated concurrently. They must be cooperated seamlessly to increase the system performance and to avoid deadlocks. In this paper, the behaviors of the intrabay automation systems are modeled using the distributed agent oriented Petri net (DAOPN). The fab intrabay DAOPN models are categorized into the new-order generation, OHT vehicle dispatching, zone control, path decision, and tool processing agent models. Finally, the DAOPN is defined, and the modeling procedures of the intrabay behaviors are all illustrated. Consequently, an experimental intrabay OHT loop is discussed and evaluated using the proposed DAOPN models and the DAOPN simulator.

关键词： dispatching distributed multiagent system modeling overhead hoist transport Petri net

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Predefined-time distributed event-triggered algorithms for resource allocation

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IET CYBER-PHYSICAL systemS: THEORY & APPLICATIONS 2022年第4期7卷 183-196页

作者： Shi, Xiasheng Xu, Lei Yang, Tao Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equip Minist Educ Dalian Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

The resource allocation problem in a distributed multi-agent system is considered in this study. First, the authors develop a predefined-time distributed algorithm and analyse its convergence analysis using the Lyapunov stability theory, in which the local constraint is ensured by a differential projection operator. Thus, a predefined time is obtained by a time-varying time-based generator. Second, to reduce the communication consumption between agents, the authors develop a static as well as a dynamic-based event-triggered control scheme, where the information broadcast only occurs at some discrete time instants. Moreover, the three proposed algorithms converge precisely to the global optimal solution. Besides, the Zeno behaviour is excluded in the above static and dynamic event-triggered mechanisms. Finally, the authors test the proposed algorithms' efficiency based on the provided numerical examples.

关键词： Lyapunov stability theory time-varying systems distributed multiagent system predefined-time distributed event-triggered algorithms multi-agent systems distributed algorithms predefined-time distributed algorithm convergence analysis stability differential projection operator distributed control resource allocation problem static event-triggered mechanisms convergence resource allocation Lyapunov methods dynamic-based event-triggered control scheme time-varying time-based generator

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A distributed Architecture for Multimodal Emotion Identification

A Distributed Architecture for Multimodal Emotion Identifica...

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13th International Conference on Practical Applications of Agents, Multi-Agent systems, and Sustainability (PAAMS)

作者： Sokolova, Marina V. Fernandez-Caballero, Antonio Lopez, Maria T. Martinez-Rodrigo, Arturo Zangroniz, Roberto Manuel Pastor, Jose Univ Castilla La Mancha Inst Invest Informat Albacete Albacete 02071 Spain Univ Castilla La Mancha Inst Tecnol Audiovisuales Cuenca 16071 Spain

ISBN: (纸本)9783319196299;9783319196282

This paper introduces a distributed multiagent system architecture for multimodal emotion identification, which is based on simultaneous analysis of physiological parameters from wearable devices, human behaviors and activities, and facial micro-expressions. Wearable devices are equipped with electrodermal activity, electrocardiogram, heart rate, and skin temperature sensor agents. Facial expressions are monitored by a vision agent installed at the height of the human's head. Also, the activity of the user is monitored by a second vision agent mounted overhead. The emotion is refined as a cooperative decision taken at a central agent node denominated "Central Emotion Detection Node" from the local decision offered by the three agent nodes called "Face Expression Analysis Node", "Behavior Analysis Node" and "Physiological Data Analysis Node". This way, the emotion identification results are outperformed through an intelligent fuzzy-based decision making technique.

关键词： distributed multiagent system multimodal architecture emotion identification

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Probability Collectives: A distributed Optimization Approach for Constrained Problems

Probability Collectives: A Distributed Optimization Approach...

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2010 IEEE World Congress on Computational Intelligence

作者： Kulkarni, Anand Jayant Tai, Kang Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore

ISBN: (纸本)9781424481262

A complex system may be controlled and optimized in a more efficient and manageable fashion by treating it as a distributed Multi-Agent system (MAS). But the major challenge in such an approach is to make the agents work in a coordinated way to optimize the system objective via optimizing their individual local goals. This paper describes the modified Probability Collectives (PC) as an evolutionary and distributed approach to achieve the system objective. The approach is validated solving a combinatorial optimization problem such as the Single Depot Multiple Traveling Salesmen Problem (MTSP). Moreover, as constraint handling in evolutionary systems has remained a challenge for years, an effort towards developing a generalized technique incorporating constraints into the PC approach is also attempted. It is validated by solving the practical problem of a spring design. The optimum results are obtained at a reasonable computational cost.

关键词： combinatorial optimization problem complex system constraint handling constraint handling design engineering distributed multiagent system distributed optimization approach evolutionary approach evolutionary computation multi-agent systems probability probability collectives single depot multiple traveling salesmen problem spring design springs (mechanical) travelling salesman problems

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