JAIS 2014 - Special Issue on “Aerospace and Mechanical Applications of Reinforcement Learning and Adaptive Learning Based Control”

Special Issue on “Aerospace and Mechanical Applications of Reinforcement Learning and Adaptive Learning Based Control”
The Journal of Aerospace Information Systems (formerly the Journal of Aerospace Computing, Information, and Communication (JACIC)) is devoted to the applied science and engineering of aerospace computing, information, and communication. Original archival research papers are sought which include significant scientific and technical knowledge and concepts. The Journal publishes qualified papers in areas such as aerospace systems and software engineering; verification and validation of embedded systems; the field known as ‘big data,’ data analytics, machine learning, and knowledge management for aerospace systems; human-automation interaction and systems health
management for aerospace systems. Applications of autonomous systems, systems engineering principles, and safety and mission assurance are of particular interest. Articles are sought which demonstrate the application of recent research in computing, information, and communications technology to a wide range of practical aerospace problems in the analysis and design of vehicles, onboard avionics, ground-based processing and control systems, flight simulation, and air transportation systems.
Key research areas included in the special issue are:
? Learning with limited data and/or in domains for which obtaining data is expensive or risky
? Real-time reinforcement learning with resource constraints (e.g., limited memory and
computation time)
? Use of reinforcement learning for risk sensitive or safety critical applications
? Scaling reinforcement learning to multi-agent systems
? Distributed reinforcement learning
? Adaptive learning-based control in the presence of uncertainty