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  • Generalization starts from very special descriptions and generalizes them as long as they are not incorrect, i.e. in every step some unnecessary premises are deleted from the antecedent.
  • The generalization procedure stops if no more premises to remove exist.

  • Generalization avoids the maximal-general issue of specialization, in fact it guarantees most-general descriptions.
  • However, generalization of course risks to derive final results that are not most-specific.

  • RELAX is an example of a generalization-based algorithm; references at the end of the lecture.

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