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Electronics
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Advances in Theories and Applications of Multi-Agent Systems
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Advances in Theories and Applications of Multi-Agent Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 11788

Special Issue Editor

Prof. Dr. He Cai

E-Mail Website
Guest Editor
School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
Interests: multiagent systems; robotic systems; smart grid; nonlinear systems and control; reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multiagent systems are universal in the real world, including multirobot, smart grid, and various other types of networked control systems. The distributed control of multiagent systems featuring sparse, flexible, and low-cost communication networks has become the most appealing control method, granted by its fast information development and communication technologies with fruitful results reported in the literature, such as distributed optimization, distributed estimation, distributed learning, etc.

This Special Issue focuses on the presentation of innovative results for multiagent systems characterized by key distinguished features such as unreliable and intermittent communication structures, static and dynamic agent dynamics, complex and multicontrol objectives, unstructured external disturbances and attacks, local and neighboring time-delays, etc. The purpose of this Special Issue is to collect selected papers reporting on topics including the latest distributed control frameworks, paradigms, and schemes for multiagent systems, ranging from theoretical analyses to practical applications, with a particular emphasis on multirobot systems and smart grid systems, such as distributed task assignment, collective simultaneous localization and mapping, rigid formation; the cooperative hunting of multiple unmanned ground vehicles, unmanned arial vehicles, unmanned surface vehicles, autonomous underwater vehicles, spacecrafts; and hierarchical voltage/current/power/load sharing for inverter-interfaced large-scale AC/DC islanded/grid-tied smart grid systems, where the distributed generators can be wind, solar, battery, supercapacitor, flywheel, or other sources.

Prof. Dr. He Cai 
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multirobot systems
  • smart grid
  • networked control systems
  • distributed optimization
  • distributed estimation
  • distributed learning

Published Papers (7 papers)

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Research

15 pages, 1383 KiB  
Article
Enhancing Efficiency in Hierarchical Reinforcement Learning through Topological-Sorted Potential Calculation
by Ziyun Zhou, Jingwei Shang and Yimang Li
Electronics 2023, 12(17), 3700; https://doi.org/10.3390/electronics12173700 - 01 Sep 2023
Viewed by 672
Abstract
Hierarchical reinforcement learning (HRL) offers a hierarchical structure for organizing tasks, enabling agents to learn and make decisions autonomously in complex environments. However, traditional HRL approaches face limitations in effectively handling complex tasks. Reward machines, which specify high-level goals and associated rewards for [...] Read more.
Hierarchical reinforcement learning (HRL) offers a hierarchical structure for organizing tasks, enabling agents to learn and make decisions autonomously in complex environments. However, traditional HRL approaches face limitations in effectively handling complex tasks. Reward machines, which specify high-level goals and associated rewards for sub-goals, have been introduced to address these limitations by facilitating the agent’s understanding and reasoning with respect to the task hierarchy. In this paper, we propose a novel approach to enhance HRL performance through topologically sorted potential calculation for reward machines. By leveraging the topological structure of the task hierarchy, our method efficiently determines potentials for different sub-goals. This topological sorting enables the agent to prioritize actions leading to the accomplishment of higher-level goals, enhancing the learning process. To assess the efficacy of our approach, we conducted experiments in the grid-world environment with OpenAI-Gym. The results showcase the superiority of our proposed method over traditional HRL techniques and reward machine-based reinforcement learning approaches in terms of learning efficiency and overall task performance. Full article
(This article belongs to the Special Issue Advances in Theories and Applications of Multi-Agent Systems)
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16 pages, 1618 KiB  
Article
Distributed Adaptive Fault-Tolerant Control for Leaderless/Leader–Follower Multi-Agent Systems against Actuator and Sensor Faults
by Zhengyu Ye, Yuehua Cheng, Ziquan Yu and Bin Jiang
Electronics 2023, 12(13), 2924; https://doi.org/10.3390/electronics12132924 - 03 Jul 2023
Cited by 2 | Viewed by 866
Abstract
The faults of actuators and sensors can lead to abnormal operations or even system faults in multi-agent systems (MASs). To address this issue, this paper proposes an adaptive fault-tolerant control (FTC) algorithm for leaderless/leader–follower MASs against actuator and sensor faults. First, extended states [...] Read more.
The faults of actuators and sensors can lead to abnormal operations or even system faults in multi-agent systems (MASs). To address this issue, this paper proposes an adaptive fault-tolerant control (FTC) algorithm for leaderless/leader–follower MASs against actuator and sensor faults. First, extended states integrating the fault components are constructed and the MAS is transformed into a descriptor system form. Then, a sliding-mode observer is designed for the transformed MAS. Based on the estimated MAS states and faults, adaptive FTC algorithms are developed, which update the control gains with the distributed tracking error. Finally, numerical simulations demonstrate that the proposed method can guarantee MAS stability against actuator and sensor faults. Full article
(This article belongs to the Special Issue Advances in Theories and Applications of Multi-Agent Systems)
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19 pages, 2455 KiB  
Article
Emergent Cooperation and Strategy Adaptation in Multi-Agent Systems: An Extended Coevolutionary Theory with LLMs
by I. de Zarzà, J. de Curtò, Gemma Roig, Pietro Manzoni and Carlos T. Calafate
Electronics 2023, 12(12), 2722; https://doi.org/10.3390/electronics12122722 - 18 Jun 2023
Cited by 3 | Viewed by 4873
Abstract
The increasing complexity of Multi-Agent Systems (MASs), coupled with the emergence of Artificial Intelligence (AI) and Large Language Models (LLMs), have highlighted significant gaps in our understanding of the behavior and interactions of diverse entities within dynamic environments. Traditional game theory approaches have [...] Read more.
The increasing complexity of Multi-Agent Systems (MASs), coupled with the emergence of Artificial Intelligence (AI) and Large Language Models (LLMs), have highlighted significant gaps in our understanding of the behavior and interactions of diverse entities within dynamic environments. Traditional game theory approaches have often been employed in this context, but their utility is limited by the static and homogenous nature of their models. With the transformative influence of AI and LLMs on business and society, a more dynamic and nuanced theoretical framework is necessary to guide the design and management of MASs. In response to this pressing need, we propose an Extended Coevolutionary (EC) Theory in this paper. This alternative framework incorporates key aspects of coevolutionary dynamics, adaptive learning, and LLM-based strategy recommendations to model and analyze the strategic interactions among heterogeneous agents in MASs. It goes beyond game theory by acknowledging and addressing the diverse interactions (economic transactions, social relationships, information exchange) and the variability in risk aversion, social preferences, and learning capabilities among entities. To validate the effectiveness of the EC framework, we developed a simulation environment that enabled us to explore the emergence of cooperation and defection patterns in MASs. The results demonstrated the potential of our framework to promote cooperative behavior and maintain robustness in the face of disruptions. The dynamics and evolution of the Multi-Agent System over time were also visualized using advanced techniques. Our findings underscore the potential of harnessing LLMs to facilitate cooperation, enhance social welfare, and promote resilient strategies in multi-agent environments. Moreover, the proposed EC framework offers valuable insights into the interplay between strategic decision making, adaptive learning, and LLM-informed guidance in complex, evolving systems. This research not only responds to the current challenges faced in modeling MASs, but also paves the way for future research in this rapidly developing field. Full article
(This article belongs to the Special Issue Advances in Theories and Applications of Multi-Agent Systems)
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19 pages, 645 KiB  
Article
Automated Bilateral Trading of Energy by Alliances in Multi-Agent Electricity Markets
by Hugo Algarvio
Electronics 2023, 12(11), 2367; https://doi.org/10.3390/electronics12112367 - 24 May 2023
Cited by 1 | Viewed by 1002
Abstract
In liberalized markets, consumers can choose their electricity suppliers or be part of an energy community. The problem with communities is that they may not have enough weight to trade in markets, which can be overcome by forming coalitions. Electricity is traded in [...] Read more.
In liberalized markets, consumers can choose their electricity suppliers or be part of an energy community. The problem with communities is that they may not have enough weight to trade in markets, which can be overcome by forming coalitions. Electricity is traded in spot markets or through bilateral contracts involving consumers and suppliers. This paper is devoted to bilateral contracting, modeled as a negotiation process involving an iterative exchange of offers and counter-offers. It focuses on coalitions of energy communities. Specifically, it presents team and single-agent negotiation models, where each consumer has strategies, tactics, and decision models. Coalition agents are equipped with intra-team strategies and decision protocols. It also describes a study of bilateral contracts involving a seller agent and a coalition of energy communities. By allying into a coalition, members of energy communities reduced their average costs for electricity by between 2% (large consumers) and 64% (small consumers) according to their consumption. Their levelized cost reduction was 19%. The results demonstrate the power of coalitions when negotiating bilateral contracts and the benefit of a low-consumption members alliance with larger players. Full article
(This article belongs to the Special Issue Advances in Theories and Applications of Multi-Agent Systems)
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22 pages, 4711 KiB  
Article
Accelerating Fuzzy Actor–Critic Learning via Suboptimal Knowledge for a Multi-Agent Tracking Problem
by Xiao Wang, Zhe Ma, Lei Mao, Kewu Sun, Xuhui Huang, Changchao Fan and Jiake Li
Electronics 2023, 12(8), 1852; https://doi.org/10.3390/electronics12081852 - 13 Apr 2023
Cited by 3 | Viewed by 973
Abstract
Multi-agent differential games usually include tracking policies and escaping policies. To obtain the proper policies in unknown environments, agents can learn through reinforcement learning. This typically requires a large amount of interaction with the environment, which is time-consuming and inefficient. However, if one [...] Read more.
Multi-agent differential games usually include tracking policies and escaping policies. To obtain the proper policies in unknown environments, agents can learn through reinforcement learning. This typically requires a large amount of interaction with the environment, which is time-consuming and inefficient. However, if one can obtain an estimated model based on some prior knowledge, the control policy can be obtained based on suboptimal knowledge. Although there exists an error between the estimated model and the environment, the suboptimal guided policy will avoid unnecessary exploration; thus, the learning process can be significantly accelerated. Facing the problem of tracking policy optimization for multiple pursuers, this study proposed a new form of fuzzy actor–critic learning algorithm based on suboptimal knowledge (SK-FACL). In the SK-FACL, the information about the environment that can be obtained is abstracted as an estimated model, and the suboptimal guided policy is calculated based on the Apollonius circle. The guided policy is combined with the fuzzy actor–critic learning algorithm, improving the learning efficiency. Considering the ground game of two pursuers and one evader, the experimental results verified the advantages of the SK-FACL in reducing tracking error, adapting model error and adapting to sudden changes made by the evader compared with pure knowledge control and the pure fuzzy actor–critic learning algorithm. Full article
(This article belongs to the Special Issue Advances in Theories and Applications of Multi-Agent Systems)
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20 pages, 2500 KiB  
Article
Spatial Path Smoothing for Car-like Robots Using Corridor-Based Quadratic Optimization
by Yongkang Lu, Yuanqing Wu, Wenjian Zhong, Yanzhou Li and Meng Chen
Electronics 2023, 12(4), 819; https://doi.org/10.3390/electronics12040819 - 06 Feb 2023
Viewed by 1074
Abstract
The global planning module of car-like robots usually plans a coarse spatial path, which may be non-smooth and kinematically infeasible for car-like robots. This study proposes an efficient spatial path smoothing approach, which is capable of optimizing a rough spatial path to be [...] Read more.
The global planning module of car-like robots usually plans a coarse spatial path, which may be non-smooth and kinematically infeasible for car-like robots. This study proposes an efficient spatial path smoothing approach, which is capable of optimizing a rough spatial path to be a high quality one. Two novel designs contribute to the proposed approach. One is a direct corridor construction method that provides an optimization region for path optimization. Based on a redefined path representation in the generated corridor, the second one is a core spatial path optimization method, where the optimization problem is formulated as a multi-objective quadratic programming (QP) with corridor and maximum-curvature constraints. Meanwhile, integrating a two-steps strategy into an optimization process yields a good trade-off between efficiency and quality. Experiment results validate that the proposed approach has the ability to online generate a high quality path. Full article
(This article belongs to the Special Issue Advances in Theories and Applications of Multi-Agent Systems)
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24 pages, 1484 KiB  
Article
Behavior-Based Herding Algorithm for Social Force Model Based Sheep Herd
by He Cai, Yaqi He, Jinye Wu and Huanli Gao
Electronics 2023, 12(2), 285; https://doi.org/10.3390/electronics12020285 - 05 Jan 2023
Viewed by 1584
Abstract
Inspired by real-world sheepdog herding behavior, in this paper, four behavior-based herding algorithms have been proposed for the social force model-based sheep herd. First, a basic behavior-based herding algorithm is designed where four types of critical sheep are rigorously defined. The decision of [...] Read more.
Inspired by real-world sheepdog herding behavior, in this paper, four behavior-based herding algorithms have been proposed for the social force model-based sheep herd. First, a basic behavior-based herding algorithm is designed where four types of critical sheep are rigorously defined. The decision of the sheepdog is made by constantly checking the positions of these four critical sheep. Then, on top of this basic herding algorithm, two extra mechanisms are considered to improve the performance of the basic herding algorithm, namely the dynamic far-end mechanism and the pausing mechanism, thus, forming the other three herding algorithms. The dynamic far-end mechanism helps to avoid the undesired circling behavior of the sheepdog around the destination area, while the pausing mechanism can greatly reduce the control cost of the sheepdog. To validate the effectiveness of the proposed herding algorithms, comprehensive tests have been conducted. The performance of the four algorithms is evaluated and compared from three aspects, namely, success rate, completion step, and control cost. Moreover, parameter analysis is provided to examine how different design parameters will affect the performance of the proposed algorithm. Finally, it is shown that when the size of the sheep herd increases, as expected, it takes more time and control effort to complete herding. Full article
(This article belongs to the Special Issue Advances in Theories and Applications of Multi-Agent Systems)
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