Learning is the fundamental and most important element for biological intelligent systems. To develop brain-like intelligent systems that are able to adaptively learn from uncertain environments to accumulate knowledge and predict future outcome to accomplish the desired goal is the long-term objective of the research in the domain of machine intelligence. In recent years, with the theoretical advances in artificial intelligence, machine learning, as well as related fields such as control theory, operation research, biomedical research, neuroscience, statistics and complex systems, intelligent learning systems have been more and more widely used in many real-world applications including nonlinear complex control, robotics, intelligent transportation systems, autonomic computing for information systems, national security, and many other fields. However, due to the complexity of real-world problems, the development of intelligent learning systems still remain a challenging topic. Therefore, it is critical to develop new theories, algorithms and applications of intelligent learning systems to promote the research in this domain, and benefit the long-term development goal of this research community.
The Journal of Intelligence Learning Systems and Applications (JILSA) is a peer reviewed international journal with a key objective to provide the academic and industrial community a medium for presenting original cutting-edge research related to intelligent learning systems and their applications. JILSA invites authors to submit their original and unpublished work that communicates current research on intelligent learning systems both in the theoretical and methodological aspects, as well as various applications in real-world applications.
Papers are invited on the topics including, but not limited to:
Theory and Algorithms for Intelligent Learning Systems: