Special Session 2. Foundations and Advances in Intelligent Decision-Making and Learning Systems

 1. Brief Description

Intelligent decision-making and learning systems form a core foundation of modern computer theory and engineering, underpinning advances in artificial intelligence, optimization, and complex system analysis. This special session focuses on theoretical models, algorithmic developments, and practical frameworks that enable intelligent systems to learn from data, reason under uncertainty, and support robust decision-making. Topics of interest include learning theory, multicriteria and multiagent decision models, uncertainty representation, and computational intelligence techniques, as well as their applications in engineering and information systems. The session aims to bring together researchers working on foundational principles and innovative methodologies that bridge theory and practice, fostering discussion on emerging challenges and future directions in intelligent learning and decisionoriented systems.


 2. Special Session Chair

Assoc. Prof. Yilun Shang, Northumbria University, UK


 3. Special Session Topics

Potential topics of interest include but are not limited to:
▪ Theoretical foundations of intelligent decision-making
▪ Learning theory and optimization in AI systems
▪ Multicriteria and multiagent decision models
▪ Uncertainty modeling and reasoning under incomplete information
▪ Intelligent algorithms for complex system analysis
▪ Decision support systems and explainable AI
▪ Mathematical and computational models for adaptive systems
▪ Applications of learning and decision models in engineering systems


 4. Introduction of Special Session Chair

Assoc. Prof. Yilun Shang, Northumbria University, UK

Yilun Shang received the B.S. and Ph.D. degrees in Mathematics from Shanghai Jiao Tong University, China, in 2005 and 2010, respectively. He is currently an Associate Professor and Head of Subject in Informatics and Artificial Intelligence at Northumbria University, UK. His research interests include complex networks and systems, probability and analysis, algebra and combinatorics, optimization and control, data science, and artificial intelligence. His work focuses on the theoretical foundations and modeling of complex and intelligent systems. He has served as a Section Editor for PeerJ Computer Science, Science Progress, and PLoS Complex Systems, and regularly reviews for international journals and conferences.