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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: 20 May 2025 | Viewed by 7614

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 Issues, Collections and Topics in MDPI journals

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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Published Papers (6 papers)

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Research

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26 pages, 1579 KiB  
Article
Understanding Recruiters’ Acceptance of Artificial Intelligence: Insights from the Technology Acceptance Model
by Filomena Almeida, Ana Junça Silva, Sara L. Lopes and Isabel Braz
Appl. Sci. 2025, 15(2), 746; https://doi.org/10.3390/app15020746 - 14 Jan 2025
Viewed by 819
Abstract
The integration of new technologies in professional contexts has emerged as a critical determinant of organizational efficiency and competitiveness. In this regard, the application of Artificial Intelligence (AI) in recruitment processes facilitates faster and more accurate decision-making by processing large volumes of data, [...] Read more.
The integration of new technologies in professional contexts has emerged as a critical determinant of organizational efficiency and competitiveness. In this regard, the application of Artificial Intelligence (AI) in recruitment processes facilitates faster and more accurate decision-making by processing large volumes of data, minimizing human bias, and offering personalized recommendations to enhance talent development and candidate selection. The Technology Acceptance Model (TAM) provides a valuable framework for understanding recruiters’ perceptions of innovative technologies, such as AI tools and GenAI. Drawing on the TAM, a model was developed to explain the intention to use AI tools, proposing that perceived ease of use and perceived usefulness influence attitudes toward AI, which subsequently affect the intention to use AI tools in recruitment and selection processes. Two studies were conducted in Portugal to address this research objective. The first was a qualitative exploratory study involving 100 interviews with recruiters who regularly utilize AI tools in their professional activities. The second study employed a quantitative confirmatory approach, utilizing an online questionnaire completed by 355 recruiters. The qualitative findings underscored the transformative role of AI in recruitment, emphasizing its potential to enhance efficiency and optimize resource management. However, recruiters also highlighted concerns regarding the potential loss of personal interaction and the need to adapt roles within this domain. The results also supported the indirect effect of perceived ease of use and perceived usefulness on the use of AI tools in recruitment and selection processes via positive attitudes toward the use of these tools. This suggests that AI is best positioned as a complementary tool rather than a replacement for human decision-making. The insights gathered from recruiters’ perspectives provide actionable recommendations for organizations seeking to leverage AI in recruitment processes. Specifically, the findings show the importance of ethical considerations and maintaining human involvement to ensure a balanced and effective integration of AI tools. 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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40 pages, 1079 KiB  
Article
Context-Adaptable Deployment of FastSLAM 2.0 on Graphic Processing Unit with Unknown Data Association
by Jessica Giovagnola, Manuel Pegalajar Cuéllar and Diego Pedro Morales Santos
Appl. Sci. 2024, 14(23), 11466; https://doi.org/10.3390/app142311466 - 9 Dec 2024
Viewed by 852
Abstract
Simultaneous Localization and Mapping (SLAM) algorithms are crucial for enabling agents to estimate their position in unknown environments. In autonomous navigation systems, these algorithms need to operate in real-time on devices with limited resources, emphasizing the importance of reducing complexity and ensuring efficient [...] Read more.
Simultaneous Localization and Mapping (SLAM) algorithms are crucial for enabling agents to estimate their position in unknown environments. In autonomous navigation systems, these algorithms need to operate in real-time on devices with limited resources, emphasizing the importance of reducing complexity and ensuring efficient performance. While SLAM solutions aim at ensuring accurate and timely localization and mapping, one of their main limitations is their computational complexity. In this scenario, particle filter-based approaches such as FastSLAM 2.0 can significantly benefit from parallel programming due to their modular construction. The parallelization process involves identifying the parameters affecting the computational complexity in order to distribute the computation among single multiprocessors as efficiently as possible. However, the computational complexity of methodologies such as FastSLAM 2.0 can depend on multiple parameters whose values may, in turn, depend on each specific use case scenario ( ingi.e., the context), leading to multiple possible parallelization designs. Furthermore, the features of the hardware architecture in use can significantly influence the performance in terms of latency. Therefore, the selection of the optimal parallelization modality still needs to be empirically determined. This may involve redesigning the parallel algorithm depending on the context and the hardware architecture. In this paper, we propose a CUDA-based adaptable design for FastSLAM 2.0 on GPU, in combination with an evaluation methodology that enables the assessment of the optimal parallelization modality based on the context and the hardware architecture without the need for the creation of separate designs. The proposed implementation includes the parallelization of all the functional blocks of the FastSLAM 2.0 pipeline. Additionally, we contribute a parallelized design of the data association step through the Joint Compatibility Branch and Bound (JCBB) method. Multiple resampling algorithms are also included to accommodate the needs of a wide variety of navigation scenarios. 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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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 2487
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 1586
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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Review

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22 pages, 652 KiB  
Review
A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection
by Ercan Oztemel and Muhammed Isik
Appl. Sci. 2025, 15(3), 1356; https://doi.org/10.3390/app15031356 - 28 Jan 2025
Viewed by 493
Abstract
The use of credit cards plays a crucial role in cash management and in meeting the needs for individual and commercial customers due to the spread of risks to the future by making monthly instalments instead of cash transactions. The use of credit [...] Read more.
The use of credit cards plays a crucial role in cash management and in meeting the needs for individual and commercial customers due to the spread of risks to the future by making monthly instalments instead of cash transactions. The use of credit cards therefore provides benefits not only to the customers but also to the banks as it enables and sustains a long-term relationship in between them. Despite the increase in the use of credit cards, there is also a significant increase in fraud transactions. To detect and prevent possible fraud operations, banks generally use rule-based techniques or analytical models. In this respect, analytical models have an important place due to their effectiveness, performance, and fast response. The main aim of this paper is therefore to enhance the theoretical and practical understanding of credit card fraud operations, review basic approaches, and propose a more comprehensive approach utilizing the agents. Note that in this study, static analytic modelling (existing approaches) and dynamic analytic modelling (emerging approaches) techniques are compared in terms of methodology, performance, and respective approaches. Since fraud methods and transactions are constantly changing over time, it is thought that there will be an increase in the use of agent-based models with dynamic analytical capabilities. Additionally, in this paper, a proposed model and empiric study are presented for an agent-based intelligent credit card fraud detection system. Full article
(This article belongs to the Special Issue Advancements in Multi-Agent Systems and Artificial Intelligence: Methodologies, Applications, and Future Trends)

Other

Jump to: Research, Review

38 pages, 1007 KiB  
Systematic Review
A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
by Miguel S. E. Martins, João M. C. Sousa and Susana Vieira
Appl. Sci. 2025, 15(3), 1211; https://doi.org/10.3390/app15031211 - 24 Jan 2025
Viewed by 464
Abstract
This paper presents a systematic review on reinforcement learning approaches for combinatorial optimization problems based on real-world industrial applications. While this topic is increasing in popularity, explicit implementation details are not always available in the literature. The main objective of this paper is [...] Read more.
This paper presents a systematic review on reinforcement learning approaches for combinatorial optimization problems based on real-world industrial applications. While this topic is increasing in popularity, explicit implementation details are not always available in the literature. The main objective of this paper is characterizing the agent–environment interactions, namely, the state space representation, action space mapping and reward design. Also, the main limitations for practical implementation and the needed future developments are identified. The literature selected covers a wide range of industrial combinatorial optimization problems, found in the IEEE Xplore, Scopus and Web of Science databases. A total of 715 unique papers were extracted from the query. Then, out-of-scope applications, reviews, surveys and papers with insufficient implementation details were removed. This resulted in a total of 298 papers that align with the focus of the review with sufficient implementation details. The state space representation shows the most variety, while the reward design is based on combinations of different modules. The presented studies use a large variety of features and strategies. However, one of the main limitations is that even with state-of-the-art complex models the scalability issues of increasing problem complexity cannot be fully solved. No methods were used to assess risk of biases or automatically synthesize the results. 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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