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Two distinguishing features of humanlike control vis-a-vis current technological control are the ability to make use of experience while selecting a control policy for distinct situations and the ability to do so faster and faster as more experience is gained (in contrast to current technological implementations that slow down as more knowledge is stored). The notions of context and context discernment are important to understanding this human ability. Whereas methods known as adaptive control and learning control focus on modifying the design of a controller as changes in context occur, experience-based (EB) control entails selecting a previously designed controller that is appropriate to the current situation. Developing the EB approach entails a shift of the technologist's focus ldquoup a levelrdquo away from designing individual (optimal) controllers to that of developing online algorithms that efficiently and effectively select designs from a repository of existing controller solutions. A key component of the notions presented here is that of higher level learning algorithm. This is a new application of reinforcement learning and, in particular, approximate dynamic programming, with its focus shifted to the posited higher level, and is employed, with very promising results. The author's hope for this paper is to inspire and guide future work in this promising area.