Information Theory in Artificial Intelligence
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 1000
Special Issue Editor
2. Vici&C. S.p.A., 47822 Santarcangelo di Romagna, Italy
Interests: anomaly detection; predictive maintenance; information theory; explainable AI; statistical learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The convergence between Information Theory and Artificial Intelligence (AI) has emerged as a critical area of interdisciplinary research, fostering innovations in both theoretical frameworks and practical applications. Information–theoretic concepts, such as entropy, mutual information, Kullback–Leibler divergence, and the information bottleneck principle, offer rigorous tools for understanding and enhancing the learning dynamics, generalization behavior, and robustness of intelligent systems.
This Special Issue will explore the multifaceted roles of information–theoretic methods in modern AI, spanning areas such as statistical learning, deep neural networks, probabilistic modeling, and decision-making under uncertainty. In particular, we seek contributions that advance the theoretical foundations of AI through the lens of Information Theory, as well as empirical studies that demonstrate the effectiveness of such approaches in real-world scenarios. Applications of interest include, but are not limited to, anomaly detection, predictive maintenance, representation learning, and explainable AI—areas where managing uncertainty and extracting meaningful information from complex data are paramount.
By gathering diverse perspectives from both the Artificial Intelligence and Information Theory communities, this Special Issue will foster dialogue and promote novel insights to guide the development of next-generation intelligent systems grounded in principled information–theoretic approaches.
Dr. Marco Piangerelli
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. Entropy 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 2600 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
- information theory
- artificial intelligence
- entropy
- machine learning
- mutual information
- anomaly detection
- representation learning
- predictive maintenance
- statistical inference
- explainable AI
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