On systemness and the problem solver: tutorial comments
IEEE Transactions on Systems, Man and Cybernetics
Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Optimal, predictive, and adaptive control
Optimal, predictive, and adaptive control
Modern control engineering (3rd ed.)
Modern control engineering (3rd ed.)
Feedback control systems (4th ed.)
Feedback control systems (4th ed.)
Applied Optimal Control and Estimation
Applied Optimal Control and Estimation
Computer Controlled Systems: Theory and Design
Computer Controlled Systems: Theory and Design
Adaptive Critic Design for Intelligent Steering and Speed Control of a 2-Axle Vehicle
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Stochastic Optimal Control: The Discrete-Time Case
Stochastic Optimal Control: The Discrete-Time Case
A retrospective on adaptive dynamic programming for control
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Higher Level Application of ADP: A Next Phase for the Control Field?
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Engineering Applications of Artificial Intelligence
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Humans have the ability to make use of experience while selecting their control actions for distinct and changing situations, and their process speeds up and have enhanced effectiveness as more experience is gained. In contrast, current technological implementations slow down as more knowledge is stored. A novel way of employing Approximate (or Adaptive) Dynamic Programming (ADP) is described that shifts the underlying Adaptive Critic type of Reinforcement Learning method ''up a level'', away from designing individual (optimal) controllers to that of developing on-line algorithms that efficiently and effectively select designs from a repository of existing controller solutions (perhaps previously developed via application of ADP methods). The resulting approach is called Higher-Level Learning Algorithm. The approach and its rationale are described and some examples of its application are given. The notions of context and context discernment are important to understanding the human abilities noted above. These are first defined, in a manner appropriate to controls and system-identification, and as a foundation relating to the application arena, a historical view of the various phases during development of the controls field is given, organized by how the notion 'context' was, or was not, involved in each phase.