Mathematical and Computing Sciences for Artificial Intelligence, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 716

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Special Issue Information

Dear Colleagues,

The field of artificial intelligence relies on a deep understanding of mathematics, statistics, and computer science to create algorithms that can learn from data and make intelligent decisions. However, due to the lack of data, bias in data, and the complexity of real-world systems, there are still many challenges in this field, including mathematical foundations and modeling in artificial intelligence, better optimization algorithms, interpretability of artificial intelligence, and building AI algorithms to solve specific application problems. Since mathematics is the foundation of artificial intelligence, and the integration of mathematical and computing sciences plays a crucial role in advancing AI research, this Special Issue aims to explore the latest developments, methodologies, and applications that highlight the synergy between mathematics, computing, and AI. We welcome original research papers addressing various aspects of mathematical and computing sciences for artificial intelligence. Topics of interest include, but are not limited to, the following:

  • Optimization algorithms and machine learning;
  • Probabilistic modeling and Bayesian inference in AI;
  • Reinforcement learning and control theory;
  • Adversarial attacks and defense in AI;
  • Game theory and AI decision;
  • Mathematical approaches to explainable AI;
  • Graph theory and network analysis for AI systems;
  • Natural language processing and computational linguistics;
  • Mathematical modeling for computer vision;
  • The applications of AI in the field of medical sciences.

Prof. Dr. Chong-Zhi Gao
Guest Editor

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Keywords

  • optimization algorithms and machine learning
  • probabilistic modeling and Bayesian inference in AI
  • reinforcement learning and control theory
  • adversarial attacks and defense in AI
  • game theory and AI decision
  • mathematical approaches to explainable AI
  • graph theory and network analysis for AI systems
  • natural language processing and computational linguistics
  • mathematical modeling for computer vision
  • the applications of AI in the field of medical sciences

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Published Papers (1 paper)

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Review

35 pages, 1127 KB  
Review
Understanding AI Agents—A Data-Driven Literature Review
by Johannes Stübinger and Fabio Metz
Mathematics 2026, 14(9), 1478; https://doi.org/10.3390/math14091478 - 28 Apr 2026
Viewed by 411
Abstract
This paper presents a systematic, data-driven literature review of research on Artificial Intelligence (AI) agents based on the top 100 Google Scholar publications related to the search terms “AI agent” and “AI agents”. The rapid advancement of AI agents, driven in particular by [...] Read more.
This paper presents a systematic, data-driven literature review of research on Artificial Intelligence (AI) agents based on the top 100 Google Scholar publications related to the search terms “AI agent” and “AI agents”. The rapid advancement of AI agents, driven in particular by recent progress in Large Language Models, has resulted in a diverse and fragmented research landscape that lacks comprehensive quantitative overviews. To address this gap, we implement and apply a fully automated, AI-driven analysis pipeline to the domain of AI agents. The collected publications are processed using a Large Language Model accessed via a Python-based Application Programming Interface (API), enabling an automated analysis of the literature without manual categorization. Based on this approach, the publications are grouped into data-driven thematic clusters reflecting dominant research perspectives in the field. Specifically, the identified clusters comprise “Architecture & Frameworks”, “Multi-Agent Systems”, “Applications”, “Safety” and “Ethics, Accountability & Governance”. By synthesizing the literature in a structured and automated manner, this work provides a consolidated overview of central research patterns, identifies key operational and structural challenges and highlights fragmentation across AI agent research. The findings support a more systematic understanding of AI agents and provide a foundation for future research on robust, scalable and trustworthy AI agent systems. Full article
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