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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Branicky, M.S. |
| Copyright Year | 1991 |
| Description | Author affiliation: AI Lab., MIT, Cambridge, MA, USA (Branicky, M.S.) |
| Abstract | 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. |
| Starting Page | 266 |
| Ending Page | 271 |
| File Size | 423299 |
| Page Count | 6 |
| File Format | |
| ISBN | 081862163X |
| DOI | 10.1109/ROBOT.1991.131585 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1991-04-09 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Inverse problems Iterative algorithms Input variables Intelligent robots Artificial intelligence Laboratories Measurement Machine learning Performance evaluation Testing |
| Content Type | Text |
| Resource Type | Article |
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