Knowledge-based Systems is an international and interdisciplinary journal in the field of artificial intelligence. The journal will publish original, innovative and creative research results in the field, and is designed to focus on research in knowledge-based and other artificial intelligence techniques-based systems with the following objectives and capabilities: to support human prediction and decision-making through data science and computation techniques; to provide a balanced coverage of both theory and practical study in the field; and to encourage new development and implementation of knowledge-based intelligence models, methods, systems, and software tools, with applications in business, government, education, engineering and healthcare.

This journal's current leading topics are but not limited to:

    Machine learning theory, methodology and algorithms
    Data science theory, methodologies and techniques
    Knowledge presentation and engineering
    Recommender systems and E-service personalization
    Intelligent decision support systems, prediction systems and warning systems
    Computational Intelligence systems
    Data-driven optimization
    Cognitive interaction and brain–computer interface
    Knowledge-based computer vision techniques

Special Issue Instructions

Knowledge-based Systems (KBS), an international and interdisciplinary peer-reviewed academic journal in the field of artificial intelligence, welcomes the submission of special issues on timely topics within the scope of the journal. The main objectives of the journal to organize special issues are to bring together state-of-the-art and high-quality research works, to promote key advances in the science and applications in the important field of knowledge-based systems, and to drive emerging research topics and establish flagships in the field.

How to submit your Special Issue proposal:

    Check the selection criteria below for a KBS special issue to make sure your proposal is relevant to the journal,
    Write your special issue proposal in the structure given below,
    Submit the special issue proposal to the Editor-in-Chief (EiC),
    The EiC and KBS special issue assessment panel will then review your proposal and reply with their decision.

Guest Editors' Duty and Special Issue Process:

After a special issue proposal is accepted by the journal, a call for papers can be formally distributed. All the papers submitted to the special issue will undergo a peer review process. Guest Editors will manage the process and ensure that the reviewing standards for Knowledge-Based Systems regular issues are maintained. A Managing Guest Editor, who will be responsible for distributing submissions to the other Guest Editors, will need to be nominated. After the Guest Editors make recommendations on each paper in the special issue, the EiC will make the final decisions of acceptance for publication. After all papers to be included in the Special Issue are accepted, the Guest Editors will be responsible for either preparing an Editorial (1–2 pages in length) or writing a field survey (5–10 pages in length), which will incorporate the selected papers and related literature relevant to the topic of the special issue.

Reproducibility Badge Initiative and Software Publication

Reproducibility Badge Initiative (RBI) is a collaboration with Code Ocean (CO), a cloud based computational reproducibility platform that helps the community by enabling sharing of code and data as a resource for non-commercial use. CO verifies the submitted code (and data) and certifies its reproducibility. Code submission will be verified by the Code Ocean team for computational reproducibility by making sure it runs, delivers results and it is self-contained. For more information please visit this help article. Note that an accepted paper will be published independently of the CO application outcome. However, if the paper receives the Reproducibility badge, it will be given additional exposure by having an attached R Badge, and by being citable at the CO website with a DOI.

We invite you to convert your open source software into an additional journal publication in Software Impacts, a multi-disciplinary open access journal. Software Impacts provides a scholarly reference to software that has been used to address a research challenge. The journal disseminates impactful and re-usable scientific software through Original Software Publications which describe the application of the software to research and the published outputs.

For more information contact us at: software.impacts@elsevier.com

Special Issue on Causal Inference for Learning and Applications
Día de Entrega: 2025-10-31

In recent years, causal inference has emerged as a critical tool for understanding cause-and-effect relationships within complex systems. By incorporating causal reasoning into machine learning, models can move beyond correlation-based learning to develop a deeper understanding of and intervention in real-world systems. This paradigm shift enables more robust, interpretable, and actionable insights, which are essential for addressing challenges in fields such as healthcare, bioinformatics, and autonomous systems.

This special issue focuses on the integration of causal inference with learning systems, highlighting its transformative potential across a wide range of fields, particularly in applications such as healthcare, bioinformatics, and decision-making systems. The special issue is aligned with the Knowledge-Based Systems (KBS) journal’s scope and aims to foster research that advances the understanding of causal reasoning in intelligent systems.

In healthcare, for example, causal inference can model patient outcomes based on medical interventions, enabling more precise treatment recommendations and improving overall patient care. Beyond healthcare, causal inference has applications in areas such as genomics, precision medicine, robotics, and complex decision-making, where understanding causal relationships is vital for designing effective strategies.

Guest editors:

Prof. Huanhuan Chen (Executive Guest Editor)
University of Science and Technology of China, Hefei, China
Email: hchen@ustc.edu.cn 

Prof. Chunyan Miao
Nanyang Technological University, Singapore
Email: ASCYMiao@ntu.edu.sg 

Prof. Peter Tino
University of Birmingham, Birmingham, UK
Email: p.tino@bham.ac.uk  

Prof. Mengjie Zhang
Victoria University of Wellington, Wellington, New Zealand
Email: mengjie.zhang@ecs.vuw.ac.nz 

Prof. Xin Yao
Lingnan University, Hong Kong, China
Email: xinyao@ln.edu.hk 

Special issue information:

This special issue aims to highlight state-of-the-art research on causal inference and its applications in learning systems and other critical domains. We invite submissions that address, but are not limited to, the following topics:

    Theories and methods for causal inference in learning systems
    Learning causal structures in complex systems
    Learning causal structures using evolutionary algorithms
    Integration of counterfactual reasoning in machine learning
    Applications of causal inference in healthcare, including precision medicine and disease modeling
    Optimization under uncertainty using causal models
    Explainable AI via causal reasoning
    Real-world applications of causal inference in genomics, bioinformatics, robotics, and personalized medicine

Manuscript submission information:

Important Dates:

Submission Open Date: April 1, 2025
Manuscript Submission Deadline: October 31, 2025
Completion of Review and Revision Process: March 31, 2026
Final Notification: April 30, 2026

Contributed papers must be submitted via the Knowledge-Based Systems online submission system (Editorial Manager®): Please select the article type “VSI: Causal Inference for Learning and Applications” when submitting the manuscript online.

Please refer to the Guide for Authors to prepare your manuscript.

For any further information, the authors may contact the Guest Editors.

Keywords:

Causal Inference, counterfactual reasoning, explainable learning