Knowledge-Driven Artificial Intelligence: Models, Optimization and Algorithms

Dear Colleagues,

The proliferation of artificial intelligence, especial large-scale language models, has redefined problem-solving paradigms, offering revolutionary tools to tackle complex challenges in virtually every sector. This Topic Collection, “Knowledge-Driven Artificial Intelligence: Models, Optimization and Algorithms”, aims to showcase the breadth and depth of AI's impact through interdisciplinary contributions spanning healthcare, urban planning, finance, manufacturing, and beyond. Topics include an explainable AI for clinical decision-making, computer vision in autonomous systems, natural language processing for global communication, and AI-driven design optimization in smart cities. Submissions may explore hybrid models that combine reinforcement learning with symbolic AI, federated learning for decentralized data ecosystems, or neuro-symbolic systems that bridge human intuition and machine precision. We encourage work that advances mathematical foundations—such as topological data analysis for anomaly detection, game-theoretic frameworks for resource allocation, or probabilistic programming for uncertainty management—while grounding innovations in empirical validation across various domains. By curating cutting-edge research that transcends traditional disciplinary boundaries, this Special Issue aims to map the evolving landscape of AI as a universal catalyst for innovation and societal progress.

Prof. Dr. Huawen Liu
Dr. Qing Li
Collection Editors

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