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Advancements in Multi-agent Systems and Artificial Intelligence: Methodologies, Applications, and Future Trends

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3562

Special Issue Editor

Dr. André Sales Mendes

E-Mail Website
Guest Editor
Expert Systems and Applications Lab (ESALAB), Faculty of Science, University of Salamanca, 37008 Salamanca, Spain
Interests: computer languages and systems; computer science

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to our upcoming Special Issue on multi-agent systems, a crucial and growing area of research in computer science and artificial intelligence (AI). These systems consist of multiple interacting agents that can work collaboratively or competitively to achieve individual or collective goals. The study of multi-agent systems is vital due to their applications in various fields, including robotics, automated negotiation, distributed problem solving, among others.

This Special Issue aims to advance the understanding and development of multi-agent systems by exploring novel methodologies, applications, and theoretical foundations, particularly focusing on their integration with AI. The convergence of multi-agent systems and AI has the potential to revolutionize various sectors by enabling intelligent, autonomous, and cooperative solutions to complex problems.

In this Special Issue, original research articles and reviews are welcome. The research areas may include (but are not limited to) the following:

  • Coordination and collaboration mechanisms in multi-agent systems.
  • Applications of multi-agent systems in robotics and automation.
  • Agent-based modeling and simulation techniques.
  • Distributed artificial intelligence.
  • Multi-agent learning and adaptation.
  • Ethical and social implications of multi-agent systems.
  • AI-driven decision making in multi-agent environments.
  • Real-world applications and case studies integrating AI and multi-agent systems.

We encourage submissions that demonstrate novel theoretical insights, practical applications, and interdisciplinary approaches to solving complex problems using multi-agent systems and AI. This Special Issue will serve as a comprehensive resource for researchers, practitioners, and policymakers interested in the future of multi-agent systems and their synergy with artificial intelligence.

Dr. André Sales Mendes
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. Applied Sciences 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

  • multi-agent systems
  • artificial intelligence
  • coordination mechanisms
  • agent-based modeling
  • distributed AI
  • robotics
  • autonomous systems
  • ethical implications
  • AI-driven decision making

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

15 pages, 474 KiB  
Article
Federated Learning in Dynamic and Heterogeneous Environments: Advantages, Performances, and Privacy Problems
by Fabio Liberti, Davide Berardi and Barbara Martini
Appl. Sci. 2024, 14(18), 8490; https://doi.org/10.3390/app14188490 - 20 Sep 2024
Viewed by 1825
Abstract
Federated Learning (FL) represents a promising distributed learning methodology particularly suitable for dynamic and heterogeneous environments characterized by the presence of Internet of Things (IoT) devices and Edge Computing infrastructures. In this context, FL allows you to train machine learning models directly on [...] Read more.
Federated Learning (FL) represents a promising distributed learning methodology particularly suitable for dynamic and heterogeneous environments characterized by the presence of Internet of Things (IoT) devices and Edge Computing infrastructures. In this context, FL allows you to train machine learning models directly on edge devices, mitigating data privacy concerns and reducing latency due to transmitting data to central servers. However, the heterogeneity of computational resources, the variability of network connections, and the mobility of IoT devices pose significant challenges to the efficient implementation of FL. This work explores advanced techniques for dynamic model adaptation and heterogeneous data management in edge computing scenarios, proposing innovative solutions to improve the robustness and efficiency of federated learning. We present an innovative solution based on Kubernetes which enables the fast application of FL models to Heterogeneous Architectures. Experimental results demonstrate that our proposals can improve the performance of FL in IoT and edge environments, offering new perspectives for the practical implementation of decentralized intelligent systems. Full article
(This article belongs to the Special Issue Advancements in Multi-agent Systems and Artificial Intelligence: Methodologies, Applications, and Future Trends)
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22 pages, 5394 KiB  
Article
Target-Oriented Multi-Agent Coordination with Hierarchical Reinforcement Learning
by Yuekang Yu, Zhongyi Zhai, Weikun Li and Jianyu Ma
Appl. Sci. 2024, 14(16), 7084; https://doi.org/10.3390/app14167084 - 12 Aug 2024
Viewed by 1210
Abstract
In target-oriented multi-agent tasks, agents collaboratively achieve goals defined by specific objects, or targets, in their environment. The key to success is the effective coordination between agents and these targets, especially in dynamic environments where targets may shift. Agents must adeptly adjust to [...] Read more.
In target-oriented multi-agent tasks, agents collaboratively achieve goals defined by specific objects, or targets, in their environment. The key to success is the effective coordination between agents and these targets, especially in dynamic environments where targets may shift. Agents must adeptly adjust to these changes and re-evaluate their target interactions. Inefficient coordination can lead to resource waste, extended task times, and lower overall performance. Addressing this challenge, we introduce the regulatory hierarchical multi-agent coordination (RHMC), a hierarchical reinforcement learning approach. RHMC divides the coordination task into two levels: a high-level policy, assigning targets based on environmental state, and a low-level policy, executing basic actions guided by individual target assignments and observations. Stabilizing RHMC’s high-level policy is crucial for effective learning. This stability is achieved by reward regularization, reducing reliance on the dynamic low-level policy. Such regularization ensures the high-level policy remains focused on broad coordination, not overly dependent on specific agent actions. By minimizing low-level policy dependence, RHMC adapts more seamlessly to environmental changes, boosting learning efficiency. Testing demonstrates RHMC’s superiority over existing methods in global reward and learning efficiency, highlighting its effectiveness in multi-agent coordination. Full article
(This article belongs to the Special Issue Advancements in Multi-agent Systems and Artificial Intelligence: Methodologies, Applications, and Future Trends)
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