Results obtained from experiments with task-level learning are described. The main idea of task-level learning is that a given task can be viewed as an input-output system driven by a vector of input variables or commands and responding with a vector of output variables or performance indicators. This formulation allows the application of powerful numerical methods to problems at a high-level of performance measurement: the task level. Task-level learning is studied as a paradigm than may help to program machines to learn from experience in order to: (1) perform a task better over time, (2) optimize task performance, and (3) generalize knowledge over tasks. Some extensions to the paradigm are explored. A refined model learning scheme is presented. Simulation experiments are performed to test the effects of different inverse models, different learning schemes, and different learning intervals. A framework for dealing with tasks that inherently try to minimize or maximize performance is presented.
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