About the Journal

The Journal of Studies in Machine Learning and Reasoning Technologies (JSMART) is dedicated to pioneering research at the critical intersection of data-driven learning and logical reasoning. In an era where artificial intelligence is rapidly advancing, there is a growing need to move beyond purely statistical models towards systems that can also understand, explain, and reason about the world. JSMART serves as a vital platform for this discourse, fostering the development of truly intelligent and trustworthy AI.
A primary focus of the journal is the exploration of Explainable AI (XAI) and Neuro-Symbolic Integration. This involves seamlessly uniting the sub-symbolic power of deep learning and neural networks with the transparent, rule-based structures of symbolic AI. By bridging this gap, JSMART champions research that enables AI systems to not only deliver accurate predictions but also provide clear, logical justifications for their outputs, thereby enhancing their reliability and usability in critical applications.
The scope of JSMART extends to a wide array of reasoning technologies and their fusion with learning paradigms. This includes advanced work on knowledge representation, ontologies, and semantic reasoning, which allow machines to structure and comprehend complex information. Furthermore, the journal covers probabilistic reasoning, logical inference, and case-based reasoning, investigating how these classical AI approaches can be enhanced and scaled through modern machine-learning techniques to solve more complex, real-world problems.
Ultimately, JSMART provides a forum for showcasing cutting-edge applications that demonstrate the practical power of integrating learning and reasoning. These applications span diverse fields such as intelligent decision support systems, advanced robotics, sophisticated natural language understanding, and semantic web technologies. By highlighting both theoretical advances and their practical implementations, the journal aims to accelerate the development of AI that is not only powerful but also rational, accountable, and aligned with human understanding.

The Journal of Studies in Machine Learning and Reasoning Technologies (JSMART) employs a rigorous double-blind peer review process to uphold the highest standards of academic integrity and scholarly excellence. Upon submission, each manuscript undergoes an initial check by the editorial office for technical compliance and scope alignment. If suitable, the Editor-in-Chief assigns it to an Associate Editor with relevant expertise, who then invites at least two independent and anonymous experts to review the paper. Throughout this stage, the identities of both the authors and the reviewers are concealed from each other to ensure impartiality. These reviewers evaluate the manuscript based on its originality, methodological soundness, validity of reasoning, clarity of presentation, and significance to the field. Based on the reviewers' detailed reports and their own assessment, the Associate Editor makes a final recommendation, leading to a decision that can be acceptance, minor/major revision, or rejection. This comprehensive and critical process is fundamental to maintaining the journal's reputation for publishing high-quality, impactful research in machine learning and reasoning technologies.