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Applied Sciences
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Future Horizons in Multi-Agent Systems: Pioneering Trends and Breakthrough Innovations
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Future Horizons in Multi-Agent Systems: Pioneering Trends and Breakthrough Innovations

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 3149

Special Issue Editor

Dr. Pilar Herrero-Martín

E-Mail Website
Guest Editor
Information Technology School, Technical University of Madrid, 28040 Madrid, Spain
Interests: interaction and collaboration in distributed systems; intelligent tutoring systems; HCI; multi-agent systems (MAS)

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to our Special Issue, which will focus on recent advances, innovative applications, and forward-looking research in the rapidly evolving field of Multi-Agent Systems (MASs).

Multi-Agent Systems represent a leading-edge area within artificial intelligence, robotics, distributed computing, and intelligent automation. They are transforming the design and implementation of adaptive, autonomous, and collaborative technologies across a wide range of domains, including smart cities, education, finance, healthcare, and beyond.

We welcome the submission of high-quality original research articles, comprehensive review papers, and case studies. Topics of interest include, but are not limited to, the following:

  • Agent Architectures and Learning Paradigms: Scalable frameworks, decentralized decision-making, and reinforcement learning.
  • Collaboration, Coordination, and Communication: Multi-agent teamwork, negotiation strategies, and explainable human–agent interaction.
  • Ethics, Security, and Resilience: Trust, safety, privacy, and robustness in adversarial or mission-critical environments.
  • Smart Environments and IoT Applications: MAS solutions for intelligent urban planning, smart grids, and interconnected ecosystems.
  • Swarm Intelligence and Robotics: Bio-inspired agents and distributed multi-robot systems.
  • Industrial and Societal Applications: MASs in business, finance, supply chains, manufacturing, healthcare, and emergency response.
  • MASs in Education: Intelligent tutoring systems, personalized learning agents, and learning analytics.
  • Emerging and Hybrid Technologies: Cognitive agents, hybrid AI models, and quantum-inspired MASs.

This Special Issue aims to serve as a prominent platform for researchers, practitioners, and policymakers dedicated to shaping the future of multi-agent systems. The selected contributions will play a critical role in advancing the next generation of research and innovation in this dynamic and interdisciplinary field.

Dr. Pilar Herrero-Martín
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 (MASs)
  • agent architectures
  • collaboration and coordination
  • human–agent interaction
  • swarm intelligence
  • intelligent tutoring systems
  • security and privacy
  • hybrid and quantum-inspired agents

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

Published Papers (4 papers)

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Research

31 pages, 3243 KB  
Article
Towards Intelligent Care: Computational Multi-Agent Architectures for Digital Management of Anxiety Episodes and Personal Well-Being
by María García-Ocón and Pilar Herrero-Martín
Appl. Sci. 2025, 15(19), 10544; https://doi.org/10.3390/app151910544 - 29 Sep 2025
Viewed by 350
Abstract
The future of anxiety management lies in bridging traditional evidence-based treatments with intelligent and adaptive digital platforms. Embedding multi-agent systems capable of real-time mood detection and self-management support represents a transformative step towards intelligent care, enabling users to independently regulate acute episodes, prevent [...] Read more.
The future of anxiety management lies in bridging traditional evidence-based treatments with intelligent and adaptive digital platforms. Embedding multi-agent systems capable of real-time mood detection and self-management support represents a transformative step towards intelligent care, enabling users to independently regulate acute episodes, prevent relapse, and promote sustained personal well-being. These digital solutions illustrate how technology can improve accessibility, personalization, and adherence, while establishing the foundation for integrating multi-agent architectures into mental health systems. Such architectures can continuously detect and interpret users’ emotional states through multimodal data, coordinating specialized agents for monitoring, personalization, and intervention. Crucially, they extend beyond passive data collection to provide active, autonomous support during moments of heightened anxiety, guiding individuals through non-pharmacological strategies such as breathing retraining, grounding techniques, or mindfulness practices without requiring immediate professional involvement. By operating in real time, multi-agent systems function as intelligent digital companions capable of anticipating needs, adapting to context, and ensuring that effective coping mechanisms are accessible at critical moments. This paper presents a multi-agent architecture for the digital management of anxiety episodes, designed not only to enhance everyday well-being but also to deliver immediate, personalized assistance during unexpected crises, offering a scalable pathway towards intelligent, patient-centered mental health care. Full article
(This article belongs to the Special Issue Future Horizons in Multi-Agent Systems: Pioneering Trends and Breakthrough Innovations)
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36 pages, 2924 KB  
Article
Improving Performance and Robustness with Two Strategies in Self-Adaptive Differential Evolution Algorithms for Planning Sustainable Multi-Agent Cyber–Physical Production Systems
by Fu-Shiung Hsieh
Appl. Sci. 2025, 15(18), 10266; https://doi.org/10.3390/app151810266 - 21 Sep 2025
Viewed by 360
Abstract
In the real world, forming a team of two or more people to solve a problem collaboratively is common to take advantage of the complementarity of the values and skills of team members. This idea can be used to develop more effective hybrid [...] Read more.
In the real world, forming a team of two or more people to solve a problem collaboratively is common to take advantage of the complementarity of the values and skills of team members. This idea can be used to develop more effective hybrid solution algorithms for solving problems by combining different solution strategies. In the realm of metaheuristic optimization, many hybrid metaheuristic algorithms have been developed based on combining different metaheuristic solution approaches. An interesting question is to study whether arbitrarily combining two different strategies can lead to a more effective solution approach to tackle complex problems. To evaluate whether a hybrid solution algorithm created by combining two different strategies to solve a problem is effective, we studied whether the hybrid solution algorithm can improve the performance and robustness by comparing the results of the solutions obtained by the hybrid solution algorithm with those obtained by the corresponding two original single-strategy solution algorithms. More specifically, we studied whether arbitrarily combining two different DE strategies selected from four standard DE strategies can lead to a more effective solution approach for planning sustainable Cyber–Physical Production Systems (CPPSs) modeled with multi-agent systems (MASs) in terms of performance and robustness. Ten cases for testing the algorithms for planning sustainable processes in CPPSs, with up to 20 operations and up to 40 resources, were used in the experiments. We conducted experiments by applying 13 algorithms, including 6 hybrid DE algorithms and 7 existing algorithms (4 standard DE, NSDE algorithms, PSO, SaNADE), to find the solutions for 10 discrete optimization planning problems with various types of constraints. The results of the experiments show that each self-adaptive hybrid DE algorithm either outperforms or performs as well as the four standard DE algorithms, NSDE algorithm, and PSO algorithm in most test cases in terms of performance and robustness for population sizes of 30 and 50. The rankings generated through the Friedman test based on the results of the experiments also show that the rankings of the six hybrid DE algorithms created based on hybridization are better than most of the others seven existing algorithms, with only one exception. The rankings generated via the Friedman test indicate that the top 3 among the 13 algorithms are the hybrid DE algorithms. The results of this study provide a simple rule to develop a more effective hybrid DE algorithm by combining two DE strategies. Full article
(This article belongs to the Special Issue Future Horizons in Multi-Agent Systems: Pioneering Trends and Breakthrough Innovations)
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21 pages, 1813 KB  
Article
Sequential Game Model for Urban Emergency Human–Machine Collaborative Decision-Making
by Shaonan Shan, Yunsen Zhang, Jinjin Hao, Fang Zhang and Guoqiang Han
Appl. Sci. 2025, 15(18), 10083; https://doi.org/10.3390/app151810083 - 15 Sep 2025
Viewed by 420
Abstract
Decision-making algorithms based on big data, artificial intelligence and other technologies are increasingly being applied to urban emergency decision-making, and urban smart emergency response is gradually appearing to be transformed from traditional empirical decision-making to human–machine collaborative decision-making. This paper explores the motivations [...] Read more.
Decision-making algorithms based on big data, artificial intelligence and other technologies are increasingly being applied to urban emergency decision-making, and urban smart emergency response is gradually appearing to be transformed from traditional empirical decision-making to human–machine collaborative decision-making. This paper explores the motivations for cooperative decision-making between leaders (human) and followers (machines) in urban emergency management in the presence of science and technology input spillovers. It focuses on the impact of human–machine cooperative decision-making on urban emergency response capacity, science and technology inputs and total urban emergency response benefits and discusses how to maximize the total benefits of urban emergency response under different levels of spillovers. In this paper, a three-stage dynamic game model is constructed: leaders and followers decide whether to establish a cooperative decision in the first stage; decide the level of science and technology inputs in the second stage; and compete for sequential decisions in the third stage. It was found that, firstly, unlike the case of static games, in sequential games, leaders and followers develop a willingness to cooperate in decision-making only when the spillover coefficients are in the lower range. Second, cooperative human–machine decision-making may diminish the importance of human experience in urban emergency management. Finally, the effectiveness of collaborative human–machine decision-making in urban emergencies deserves further research. The research in this paper provides recommendations for smart urban emergency management. Full article
(This article belongs to the Special Issue Future Horizons in Multi-Agent Systems: Pioneering Trends and Breakthrough Innovations)
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20 pages, 1535 KB  
Article
Multi-Agentic LLMs for Personalizing STEM Texts
by Michael Vaccaro, Jr., Mikayla Friday and Arash Zaghi
Appl. Sci. 2025, 15(13), 7579; https://doi.org/10.3390/app15137579 - 6 Jul 2025
Cited by 1 | Viewed by 1473
Abstract
Multi-agent large language models promise flexible, modular architectures for delivering personalized educational content. Drawing on a pilot randomized controlled trial with middle school students (n = 23), we introduce a two-agent GPT-4 framework in which a Profiler agent infers learner-specific preferences and [...] Read more.
Multi-agent large language models promise flexible, modular architectures for delivering personalized educational content. Drawing on a pilot randomized controlled trial with middle school students (n = 23), we introduce a two-agent GPT-4 framework in which a Profiler agent infers learner-specific preferences and a Rewrite agent dynamically adapts science passages via an explicit message-passing protocol. We implement structured system and user prompts as inter-agent communication schemas to enable real-time content adaptation. The results of an ordinal logistic regression analysis hinted that students may be more likely to prefer texts aligned with their profile, demonstrating the feasibility of multi-agent system-driven personalization and highlighting the need for additional work to build upon this pilot study. Beyond empirical validation, we present a modular multi-agent architecture detailing agent roles, communication interfaces, and scalability considerations. We discuss design best practices, ethical safeguards, and pathways for extending this framework to collaborative agent networks—such as feedback-analysis agents—in K-12 settings. These results advance both our theoretical and applied understanding of multi-agent LLM systems for personalized learning. Full article
(This article belongs to the Special Issue Future Horizons in Multi-Agent Systems: Pioneering Trends and Breakthrough Innovations)
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