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Learning Description From Examples

Learning Description From Examples. Learning by Analyzing Differences: the Winston Algorithm. Artificial Intelligence Learning by Analyzing Differences. L. Manevitz. Procedure W Let description  First Sample. For all samples: If near miss use SPECIALIZE. If example use GENERALIZE.

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Learning Description From Examples

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  1. Learning Description From Examples Learning by Analyzing Differences: the Winston Algorithm

  2. Artificial IntelligenceLearning by Analyzing Differences L. Manevitz

  3. Procedure W • Let description  First Sample. • For all samples: • If near miss use SPECIALIZE. • If example use GENERALIZE.

  4. “Near-Miss” • Need to find single important difference from example to description • If cant identify the difference, then skip example

  5. Example Arch Near miss Near miss Arch

  6. A B C Wedge Brick a Structural Descriptions has-part has-part supported-by Is-a Is-a

  7. C A B D Brick Brick Brick b Structural Descriptions cont. has-part has-part has-part supported-by supported-by Is-a left-of Is-a Is-a right-of Does-not-marry

  8. C A B D Brick Brick Wedge c Structural Descriptions cont. has-part has-part has-part supported-by supported-by Is-a left-of Is-a Is-a right-of Does-not-marry

  9. must-support must-support left-of Arch a b c left-of Near miss Figure 2 support support left-of Arch

  10. must-support must-support Arch left-of must-not-touch must-not-touch c b a Figure 3 must-support must-support must-support must-support Arch Near miss left-of left-of touch touch

  11. Is-a Brick Block must-support must-support Arch left-of must-not-touch must-not-touch a Figure 4

  12. Is-a Wedge Block must-support must-support Arch left-of must-not-touch b must-not-touch Figure 4 cont.

  13. must-be-a Block must-support must-support Arch left-of must-not-touch c must-not-touch Figure 4 cont.

  14. Specialize • Match the evolving model to the sample to establish correspondences among parts.

  15. Specialize cont. • Determine whether there is a single, most important difference between the evolving model and the near miss: • If there is a single, most important difference , determine whether the evolving model or the near miss has a link that is not in the other: • If the evolving model has a link that is not in the near miss, use the require-link heuristic. • If the near miss has a link that is not in the model, use the forbid-link heuristic. • Otherwise ignore the sample.

  16. Generalize • Match the evolving model to the sample to establish correspondences among parts. • For each difference, determine the difference type: • If the difference is that the link points to a different class in the evolving model from the class the link points to in the sample, determine if the classes are part of a classification tree:

  17. Generalize cont. • If the classes are part of a classification tree, use the climb-tree heuristic. • If the classes form an exhaustive set, use the drop-link heuristic. • Otherwise, use the enlarge-set heuristic. • If the difference is that a link is missing in either the evolving model or the example, use the drop-link heuristic. • If the difference is that different numbers, or an interval and a number outside the interval, are involved, use the close-interval heuristic. • Otherwise ignore the difference.

  18. The Heuristics • The require-link heuristic. • The forbid-link heuristic. • The climb-tree heuristic. • The enlarge-set heuristic. • The drop-link heuristic. • The close-interval heuristic.

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