Agents and Multi-Agent Systems

Abstract submission deadline

30 September 2024

Manuscript submission deadline

31 December 2024

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Topic Information

Dear Colleagues,

In recent years, agent-based technology has become a popular tool for solving engineering issues. Thus far, multiagent systems (MASs) in automatic control have developed rapidly. Examples of MAS may include large crowds during public events, autonomous highways, flocks of birds, social media websites, and drones in flying formations. Typical application scenarios of MAS control may include cooperative industrial robots, coordinated unmanned systems, sensor networks, and smart grids, to name just a few. The goal of the entire system is achieved through the use of local interactions between agents. For example, the number of distributed energy components and devices in the smart grid continues to increase globally, and distributed control solutions are ideal solutions for managing and utilizing these devices and large amounts of data. While distributed control needs to go beyond traditional technologies, typical challenges of distributed control may come from limited communication and decentralized computing, thus requiring the control implementation to be sufficiently simple.

Prof. Dr. He Cai
Prof. Dr. Maobin Lv
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400	Submit
Electronics electronics	2.9	4.7	2012	15.6 Days	CHF 2400	Submit
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600	Submit
Modelling modelling	-	-	2020	15.8 Days	CHF 1000	Submit
Systems systems	1.9	3.3	2013	16.8 Days	CHF 2400	Submit

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Published Papers (4 papers)

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All Applied Sciences Electronics Energies Modelling Systems

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20 pages, 1360 KiB  

Open AccessArticle

Scalable Multi-Robot Task Allocation Using Graph Deep Reinforcement Learning with Graph Normalization

by Zhenqiang Zhang, Xiangyuan Jiang, Zhenfa Yang, Sile Ma, Jiyang Chen and Wenxu Sun

Electronics 2024, 13(8), 1561; https://doi.org/10.3390/electronics13081561 - 19 Apr 2024

Viewed by 347

Abstract

Task allocation plays an important role in multi-robot systems regarding team efficiency. Conventional heuristic or meta-heuristic methods face difficulties in generating satisfactory solutions in a reasonable computational time, particularly for large-scale multi-robot task allocation problems. This paper proposes a novel graph deep-reinforcement-learning-based approach, [...] Read more.

Task allocation plays an important role in multi-robot systems regarding team efficiency. Conventional heuristic or meta-heuristic methods face difficulties in generating satisfactory solutions in a reasonable computational time, particularly for large-scale multi-robot task allocation problems. This paper proposes a novel graph deep-reinforcement-learning-based approach, which solves the problem through learning. The framework leverages the graph sample and aggregate concept as the encoder to extract the node features in the context of the graph, followed by a cross-attention decoder to output the probability that each task is allocated to each robot. A graph normalization technique is also proposed prior to the input, enabling an easy adaption to real-world applications, and a deterministic solution can be guaranteed. The most important advantage of this architecture is the scalability and quick feed-forward character; regardless of whether cases have a varying number of robots or tasks, single depots, multiple depots, or even mixed single and multiple depots, solutions can be output with little computational effort. The high efficiency and robustness of the proposed method are confirmed by extensive experiments in this paper, and various multi-robot task allocation scenarios demonstrate its advantage. Full article

(This article belongs to the Topic Agents and Multi-Agent Systems)

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20 pages, 1916 KiB  

Open AccessArticle

Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics

by Daniel Silva-Contreras and Salvador Godoy-Calderon

Appl. Sci. 2024, 14(8), 3303; https://doi.org/10.3390/app14083303 - 14 Apr 2024

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Abstract

When autonomous agents are deployed in an unknown environment, obstacle-avoiding movement and navigation are required basic skills, all the more so when agents are limited by partial-observability constraints. This paper addresses the problem of autonomous agent navigation under partial-observability constraints by using a [...] Read more.

When autonomous agents are deployed in an unknown environment, obstacle-avoiding movement and navigation are required basic skills, all the more so when agents are limited by partial-observability constraints. This paper addresses the problem of autonomous agent navigation under partial-observability constraints by using a novel approach: Artificial Potential Fields (APF) assisted by heuristics. The well-known problem of local minima is addressed by providing the agents with the ability to make individual choices that can be exploited in a swarm. We propose a new potential function, which provides precise control of the potential field’s reach and intensity, and the use of auxiliary heuristics provides temporary target points while the agent explores, in search of the position of the real intended target. Artificial Potential Fields, together with auxiliary search heuristics, are integrated into a novel navigation model for autonomous agents who have limited or no knowledge of their environment. Experimental results are shown in 2D scenarios that pose challenging situations with multiple obstacles, local minima conditions and partial-observability constraints, clearly showing that an agent driven using the proposed model is capable of completing the navigation task, even under the partial-observability constraints. Full article

(This article belongs to the Topic Agents and Multi-Agent Systems)

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21 pages, 1086 KiB  

Open AccessArticle

Learning Ad Hoc Cooperation Policies from Limited Priors via Meta-Reinforcement Learning

by Qi Fang, Junjie Zeng, Haotian Xu, Yue Hu and Quanjun Yin

Appl. Sci. 2024, 14(8), 3209; https://doi.org/10.3390/app14083209 - 11 Apr 2024

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Abstract

When agents need to collaborate without previous coordination, the multi-agent cooperation problem transforms into an ad hoc teamwork (AHT) problem. Mainstream research on AHT is divided into type-based and type-free methods. The former depends on known teammate types to infer the current teammate [...] Read more.

When agents need to collaborate without previous coordination, the multi-agent cooperation problem transforms into an ad hoc teamwork (AHT) problem. Mainstream research on AHT is divided into type-based and type-free methods. The former depends on known teammate types to infer the current teammate type, while the latter does not require them at all. However, in many real-world applications, the complete absence and sufficient knowledge of known types are both impractical. Thus, this research focuses on the challenge of AHT with limited known types. To this end, this paper proposes a method called a Few typE-based Ad hoc Teamwork via meta-reinforcement learning (FEAT), which effectively adapts to teammates using a small set of known types within a single episode. FEAT enables agents to develop a highly adaptive policy through meta-reinforcement learning by employing limited priors about known types. It also utilizes this policy to generate a diverse type repository automatically. During the ad hoc cooperation, the agent can autonomously identify known teammate types followed by directly utilizing the pre-trained optimal cooperative policy or swiftly updating the meta policy to respond to teammates of unknown types. Comprehensive experiments in the pursuit domain validate the effectiveness of the algorithm and its components. Full article

(This article belongs to the Topic Agents and Multi-Agent Systems)

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26 pages, 10833 KiB  

Open AccessArticle

Effective Evolutionary Principles for System-of-Systems: Insights from Agent-Based Modeling in Vehicular Networks

by Junjie Liu, Junxian Liu and Mengmeng Zhang

Systems 2024, 12(3), 98; https://doi.org/10.3390/systems12030098 - 15 Mar 2024

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Abstract

System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution [...] Read more.

System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV), serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies. Full article

(This article belongs to the Topic Agents and Multi-Agent Systems)

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