International Journal of Artificial Intelligence and Agent Systems
Peer-Reviewed|Open Access

International Journal of Artificial Intelligence and Agent Systems

Building robust, trustworthy, and autonomous artificial intelligence systems requires an integrated approach that recognizes the close interdependence between AI models, intelligent agents, ...
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Open Access

Freely accessible to researchers worldwide

Rigorous Peer Review

High-quality and transparent review process

Global Community

A diverse and international author and reviewer network

Real-World Impact

Research that advances theory and practice

Int. J. Artif. Intell. Agent Syst.

A global platform for research at the intersection of intelligent systems and data science.

Building robust, trustworthy, and autonomous artificial intelligence systems requires an integrated approach that recognizes the close interdependence between AI models, intelligent agents, computational infrastructure, human oversight, and real-world deployment environments. Advances in artificial intelligence, deep learning, generative AI, large language models, and agentic systems are increasingly transforming science, industry, governance, and society, making it essential to address not only model performance and autonomy but also safety, transparency, reliability, and long-term societal impact. This includes developing AI systems that can reason, learn, collaborate, and adapt under dynamic conditions while remaining aligned with human values, operational constraints, and ethical principles. Achieving meaningful progress in artificial intelligence and autonomous systems demands attention to foundational challenges such as model robustness, explainability, generalization, alignment, computational efficiency, and responsible deployment. Equally important are broader considerations involving AI governance, safety evaluation, accountability, human-AI collaboration, access to computational resources, and the societal implications of increasingly capable intelligent systems. Addressing these challenges requires interdisciplinary collaboration that combines advances in AI theory, algorithms, architectures, and agent design with rigorous empirical validation and real-world applications. The International Journal of Artificial Intelligence and Agent Systems (IJAIAS) provides a dedicated scholarly platform to advance these objectives. With a multidisciplinary focus, the journal brings together perspectives from artificial intelligence, deep learning, generative AI, foundation models, large language models, natural language processing, computer vision, reinforcement learning, agentic systems, multi-agent systems, autonomous systems, and trustworthy AI to deepen understanding of how intelligent and autonomous systems can be designed, evaluated, governed, and deployed responsibly. IJAIAS supports research that bridges foundational innovation and practical implementation, encouraging contributions that demonstrate both scientific rigor and real-world impact. Through its editorial standards and publishing practices, IJAIAS actively promotes reproducibility, transparency, ethical responsibility, and rigorous peer review. The journal also seeks to contribute to broader global priorities by supporting research aligned with responsible AI development, digital transformation, human-centered innovation, AI safety, and trustworthy autonomous systems, recognizing the increasingly significant role that artificial intelligence and agent-based technologies play in shaping resilient, sustainable, and equitable futures. Publisher: Who Supports This Journal? The International Journal of Artificial Intelligence and Agent Systems (IJAIAS) is published and supported by Femington, an independent academic publishing organization committed to advancing open, ethical, and high-quality scholarly communication. Femington ensures that IJAIAS operates on transparent, community-driven, and sustainable publishing principles. This open-source backend enables efficient manuscript management, rigorous peer review, and long-term digital preservation, while reinforcing the journal’s commitment to accessibility and academic integrity. The publishing model allows IJAIAS to prioritize editorial independence, research quality, and global knowledge dissemination rather than commercial publishing constraints. IJAIAS aims to make academic research available: Online Immediately upon publication Free from most copyright or licensing restrictions Accepted Types of Articles The International Journal of Artificial Intelligence and Agent Systems (IJAIAS) considers the following categories of scholarly contributions for publication: Original Research / Research Articles Review Articles Case Studies Research Notes Editorials (by invitation or prior approval) For detailed formatting requirements and the exact structure, authors are required to use the official journal manuscript template available below. Manuscript Template (PDF): for formatting guidance and Word-based manuscript preparation LaTeX Template (ZIP): includes complete LaTeX source files for manuscript preparation Original Research / Research Articles Original Research Articles report substantial and novel contributions to the field and are considered primary literature. These manuscripts should present original theoretical developments, methodological innovations, algorithmic advances, empirical analyses, or system-level implementations related to artificial intelligence, agentic systems, and autonomous technologies. Submissions are expected to include clearly defined sections such as Introduction, Related Work, Methodology, Experiments or Evaluation, Results, and Discussion, along with a concluding section outlining implications and limitations. Articles are typically 6,000–8,000 words in length (excluding references). The research must demonstrate clear relevance to artificial intelligence, machine learning, deep learning, generative AI, foundation models, large language models, natural language processing, computer vision, reinforcement learning, agentic systems, or autonomous systems, with appropriate technical depth and rigor. Review Articles Review Articles provide a comprehensive and critical synthesis of existing research on a well-defined topic within artificial intelligence and autonomous systems. These articles should assess the current state of the field, identify gaps and challenges, and offer informed perspectives on future research directions. Review articles are regarded as secondary literature and are often widely read and highly cited. Review manuscripts should generally be 6,000–8,000 words (excluding references) and must demonstrate analytical depth rather than a descriptive summary of prior work. IJAIAS considers the following main types of review articles: Critical Reviews: Authors critically evaluate existing literature, highlighting theoretical, methodological, and empirical contributions, as well as unresolved challenges and limitations. Systematic or Scoping Reviews: Authors employ transparent, reproducible, and structured methodologies to identify, screen, and analyse relevant literature, with the aim of reducing bias and informing research or practice. Meta-Analyses: Authors use quantitative synthesis techniques to statistically aggregate findings from prior studies in order to derive robust conclusions about trends, effectiveness, performance, or capabilities across the literature. Case Studies Case Studies offer in-depth examinations of real-world applications, deployments, or implementations of artificial intelligence and autonomous systems. These manuscripts should focus on practical insights, lessons learned, and contextual factors influencing system design, performance, safety, adoption, and impact. Authors are expected to follow key stages, including case definition, contextual background, data collection, system implementation, evaluation methodology, interpretation of findings, and implications for practice or research. Case studies are typically 3,000–4,000 words in length (excluding references) and must clearly demonstrate relevance to AI and intelligent systems in applied settings. Research Notes Research Notes are concise manuscripts presenting preliminary findings, exploratory analyses, novel datasets, benchmark evaluations, new agent architectures, emerging AI methodologies, or proof-of-concept systems that may not yet warrant a full-length article but are of clear scholarly interest. These submissions should include a brief abstract and introductory section and may be written in a continuous format to maintain conciseness. Research Notes are generally 2,000–3,000 words (excluding references) and typically include a title, short background, methodology or approach, key results, and a brief conclusion. Research Notes are considered early-stage or proof-of-concept contributions. Editorials Editorials are typically commissioned by the Editors to address topical issues, emerging trends, policy developments, or strategic directions relevant to the journal’s scope. Authors interested in submitting an Editorial should contact the Editor-in-Chief (EIC) in advance to propose their idea for consideration. Benefits to Authors All articles published in the International Journal of Artificial Intelligence and Agent Systems (IJAIAS) are fully open access. This ensures that published work is available to read, download, and share worldwide. Key benefits include: Flexible publication fees for authors to ensure inclusivity Rigorous double-blind peer-review process Rapid editorial screening Review decisions are typically communicated within six weeks Fast and efficient online submission and review system Authors retain copyright of their published work The authors grant Femington a license to publish and identify itself as the original publisher.

3143-9977ISSN
3 daysSubmission to first decision (median)
Continuous PublishingFrequency
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Open Access Policy

Our Commitment to Open Access

The International Journal of Artificial Intelligence and Agent Systems (IJAIAS) is a fully open-access journal. All articles published under the open-access model in IJAIAS are made freely and permanently available online immediately upon publication, without subscription fees for readers or access restrictions.

This open-access policy applies exclusively to articles designated as open access and does not extend to member-only content, subscription-based journals, or restricted platform resources published by Femington.

To maintain broad accessibility while ensuring high editorial and publishing standards, the journal operates under a flexible and inclusive author-funding model for open-access publications.

IJAIAS is committed to the principle that unrestricted access to peer-reviewed scholarly research accelerates innovation, promotes transparency, supports responsible AI development, and advances the global understanding of artificial intelligence, intelligent agents, and autonomous systems.

Why Femington Supports Open Access?

Femington Academic Publishing adopts an open-access publishing model as a deliberate editorial and institutional choice, grounded in how contemporary artificial intelligence research is developed, validated, and deployed.

1. Artificial Intelligence Advances Require Immediate Accessibility

Research in artificial intelligence, generative AI, large language models, and autonomous systems evolves at an unprecedented pace. New methods, models, and evaluation frameworks can rapidly influence scientific research, industrial innovation, public policy, and societal decision-making. Restricting access delays validation, replication, and responsible adoption. Open access ensures that research published by Femington can be examined, tested, and built upon without unnecessary barriers.

2. AI Innovation Extends Beyond Traditional Academic Institutions

Many of the most significant advances in artificial intelligence originate not only from universities but also from industry laboratories, research institutes, startups, open-source communities, and independent researchers. Open access reflects Femington’s recognition that AI research is increasingly produced within a diverse and globally distributed innovation ecosystem.

By removing access barriers, IJAIAS enables researchers across sectors to contribute to and benefit from the collective advancement of AI knowledge.

3. Transparency Is Essential for Trustworthy AI

The development of trustworthy artificial intelligence requires openness in methods, evaluation procedures, datasets, benchmarks, and system limitations. Reproducibility, explainability, robustness, fairness, and safety cannot be adequately assessed when research findings remain inaccessible.

Open access supports the transparency and accountability necessary for research that increasingly influences autonomous decision-making, human-AI interaction, and societal outcomes.

4. Global Participation Strengthens AI Research

Artificial intelligence is a global field whose benefits and risks extend across geographic, economic, and institutional boundaries. Femington publishes research for an international audience, including scholars, practitioners, and policymakers working in environments with limited access to subscription-based resources.

Open access removes structural barriers to participation, enabling broader scholarly engagement, independent validation, interdisciplinary collaboration, and diverse perspectives on the development and governance of AI systems.

5. Alignment with Responsible AI and Innovation Principles

Responsible AI development requires broad access to scientific evidence, technical methodologies, and emerging best practices. Openly accessible research allows policymakers, regulators, practitioners, educators, and civil society organizations to engage with advances in AI and make informed decisions regarding their adoption and governance.

Femington views open access as a fundamental component of responsible innovation and ethical dissemination of AI research.

6. Supporting Reproducibility and Scientific Progress

Progress in artificial intelligence depends on the ability of researchers to reproduce results, compare methods, evaluate benchmarks, and build upon prior work. Open access facilitates scientific verification and accelerates cumulative knowledge creation by ensuring that published findings remain readily available to the global research community.

This is particularly important in rapidly evolving areas such as:

  • Generative AI
  • Large Language Models (LLMs)
  • Foundation Models
  • AI Agents
  • Multi-Agent Systems
  • Computer Vision
  • Natural Language Processing
  • Reinforcement Learning
  • Autonomous Systems

7. Long-Term Preservation and Scholarly Continuity

Open-access publishing enhances long-term discoverability, accessibility, and archival stability. By removing access restrictions, Femington ensures that published AI research remains available, usable, and citable over time, independent of institutional subscriptions or commercial access models.

This commitment supports the enduring value of scholarly contributions and facilitates the long-term accumulation of knowledge in artificial intelligence and autonomous systems.

8. Editorial Independence from Access-Based Gatekeeping

Femington’s editorial decisions are based exclusively on scholarly merit, methodological rigor, originality, and relevance to the field.

Open access enables IJAIAS to prioritize research quality and scientific contribution rather than perceived commercial value or subscription demand. The journal’s flexible fee structure is designed solely to support the operational costs of rigorous peer review, editorial management, digital preservation, and sustainable publishing infrastructure.

Editorial decisions remain fully independent of financial considerations, ensuring that all manuscripts are evaluated according to the same academic and ethical standards.

Advancing Open, Responsible, and Trustworthy AI Research

Through its open-access publishing model, IJAIAS seeks to foster a research environment in which scientific knowledge is openly shared, critically evaluated, and responsibly applied. The journal believes that broad access to high-quality AI research is essential for advancing innovation, supporting evidence-based decision-making, strengthening public trust, and ensuring that the benefits of artificial intelligence can be more widely understood and responsibly realized across society.

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