Definition: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
To have a well-defined learning problem, three features needs to be identified:
1. The class of tasks
2. The measure of performance to be improved
3. The source of experience
Examples
1. Checkers game: A computer program that learns to play checkers might improve its performance as measured by its ability to win at the class of tasks involving playing checkers games, through experience obtained by playing games against itself.
Fig: Checker game board
A checkers learning problem:
- Task T: playing checkers
- Performance measure P: percent of games won against opponents
- Training experience E: playing practice games against itself
2. A handwriting recognition learning problem:
- Task T: recognizing and classifying handwritten words within images
- Performance measure P: percent of words correctly classified
- Training experience E: a database of handwritten words with given classifications
3. A robot driving learning problem:
- Task T: driving on public four-lane highways using vision sensors
- Performance measure P: average distance travelled before an error (as judged by human overseer)
Training experience E: a sequence of images and steering commands recorded while observing a human driver
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