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Responsible AI: Alignment, Decentralization, and Optimization in Multi-Agent Systems Across...
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Responsible AI: Alignment, Decentralization, and Optimization in Multi-Agent Systems Across Dynamic Environments

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications".

Deadline for manuscript submissions: 23 September 2026 | Viewed by 5065

Special Issue Editor

Prof. Dr. Chansoo Kim

E-Mail Website
Guest Editor
1. AI, Information and Reasoning (AI/R) Laboratory, AI-Robot Department, University of Science and Technology (UST), Seoul 02792, Republic of Korea
2. Computational Science Centre, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
Interests: AI alignment and AI ELSI (ethics, laws, social implications); decentralization and federation of AI; optimization; causality; complex systems; information science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue “Responsible AI: Alignment, Decentralization, and Optimization in Multi-Agent Systems Across Dynamic Environments” aims to advance scholarly discussion on how AI systems can be effectively optimized in environments characterized by uncertainty, rapid change, and complexity. AI and multi-agent systems (MAS) have become increasingly vital across diverse domains, including autonomous robotics, intelligent transportation, smart grid and energy management, finance, healthcare, and digital social platforms, where agents must continuously learn, collaborate, and make both competitive and cooperative decisions in real-time.

This issue emphasizes decentralization, alignment, and optimization methodologies that enhance efficiency, scalability, robustness, and interpretability. Contributions are invited on topics such as decentralized and federated learning, reinforcement and evolutionary methods, causal reasoning, and hybrid approaches that combine symbolic and statistical techniques. In addition, submissions that demonstrate practical applications---ranging from cooperative autonomous systems and resilient infrastructures to decentralized digital ecosystems---are strongly encouraged.

Beyond these advances, this Special Issue highlights broader perspectives, particularly the ethical and societal dimensions of AI, alongside the challenge of aligning system behaviors with (human) values. AI and MAS must not only improve technical performance but also ensure trustworthiness, responsibility, and social legitimacy. While much of the existing literature assumes static settings, this collection will spotlight approaches designed for dynamic, real-world contexts, offering researchers, practitioners, and policymakers a platform to advance both the theoretical foundations and the responsible deployment of 'sovereign AI'.

Prof. Dr. Chansoo Kim
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 250 words) can be sent to the Editorial Office for assessment.

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. AI is an international peer-reviewed open access monthly 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 1800 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
  • AI alignment and trustworthiness
  • AI ethical and social implications (ethical and societal dimensions)
  • alignment with human values
  • decentralized and federated learning
  • optimization and AI
  • reinforcement and evolutionary learning
  • causal reasoning
  • robustness and adaptivity
  • dynamic environments
  • real-world AI applications and various applications such as physical AI (robotics), energy and battery, and finance and economics

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Published Papers (2 papers)

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Research

24 pages, 10541 KB  
Article
Multi-Agent Transfer Learning Based on Evolutionary Algorithms and Dynamic Grid Structures for Industrial Applications
by Marlon Löppenberg, Steve Yuwono and Andreas Schwung
AI 2026, 7(2), 62; https://doi.org/10.3390/ai7020062 - 6 Feb 2026
Viewed by 1221
Abstract
Distributed production systems have to increasingly balance economic goals such as energy efficiency and productivity with critical technical requirements such as flexibility, real-time capability, and reliability. This paper presents a novel approach for distributed optimization by means of Evolutionary State-based Potential Games with [...] Read more.
Distributed production systems have to increasingly balance economic goals such as energy efficiency and productivity with critical technical requirements such as flexibility, real-time capability, and reliability. This paper presents a novel approach for distributed optimization by means of Evolutionary State-based Potential Games with dynamic grid structures. More in detail, we leverage the combination of Potential Games which provide rigorous convergence guarantees with population-based optimization to improve the efficiency of the learning process. Specifically, we address challenges of previous approaches including inefficient best response strategies, insufficient coverage of the state–action space and the lack of knowledge transfer among agents. The developed strategies are evaluated on a industrial system of laboratory scale. The results highlight advances in evolutionary state-based knowledge transfer and an improved coverage resulting in efficient control policies. By leveraging dynamic grid structures, Evolutionary State-based Potential Games enable the maximization of weighted production targets while simultaneously eliminating process losses resulting in improvements in the considered metrics compared to state-of-the-art methods. Full article
(This article belongs to the Special Issue Responsible AI: Alignment, Decentralization, and Optimization in Multi-Agent Systems Across Dynamic Environments)
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31 pages, 1534 KB  
Article
Causal Reasoning and Large Language Models for Military Decision-Making: Rethinking the Command Structures in the Era of Generative AI
by Dimitrios Doumanas, Andreas Soularidis and Konstantinos Kotis
AI 2026, 7(1), 14; https://doi.org/10.3390/ai7010014 - 7 Jan 2026
Viewed by 3211
Abstract
Military decision-making is inherently complex and highly critical, requiring commanders to assess multiple variables in real-time, anticipate second-order effects, and adapt strategies based on continuously evolving battlefield conditions. Traditional approaches rely on domain expertise, experience, and intuition, often supported by decision-support systems designed [...] Read more.
Military decision-making is inherently complex and highly critical, requiring commanders to assess multiple variables in real-time, anticipate second-order effects, and adapt strategies based on continuously evolving battlefield conditions. Traditional approaches rely on domain expertise, experience, and intuition, often supported by decision-support systems designed by military experts. With the rapid advancement of Large Language Models (LLMs) such as ChatGPT, Claude, and DeepSeek, a new research question emerges: can LLMs perform causal reasoning at a level that could meaningfully replace human decision-makers, or should they remain human-led decision-support tools in high-stakes environments? This paper explores the causal reasoning capabilities of LLMs for operational and strategic military decisions. Unlike conventional AI models that rely primarily on correlation-based predictions, LLMs are now able to engage in multi-perspective reasoning, intervention analysis, and scenario-based assessments. We introduce a structured empirical evaluation framework to assess LLM performance through 10 de-identified real-world-inspired battle scenarios, ensuring models reason over provided inputs rather than memorized data. Critically, LLM outputs are systematically compared against a human expert baseline, composed of military officers across multiple ranks and years of operational experience. The evaluation focuses on precision, recall, causal reasoning depth, adaptability, and decision soundness. Our findings provide a rigorous comparative assessment of whether carefully prompted LLMs can assist, complement, or approach expert-level performance in military planning. While fully autonomous AI-led command remains premature, the results suggest that LLMs can offer valuable support in complex decision processes when integrated as part of hybrid human-AI decision-support frameworks. Since our evaluation directly tests this capability, this paradigm shift raises fundamental question: Is there a possibility to fully replace high-ranking officers/commanders in leading critical military operations, or should AI-driven tools remain as decision-support systems enhancing human-driven battlefield strategies? Full article
(This article belongs to the Special Issue Responsible AI: Alignment, Decentralization, and Optimization in Multi-Agent Systems Across Dynamic Environments)
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