Forming Concepts for Fast Inference

Henry Kautz, Bart Selman

Knowledge compilation speeds inference by creating tractable approximations of a knowledge base, but this advantage is lost if the approximations are too large. We show how learning concept generalizations can allow for a more compact representation of the tractable theory. We also give a general induction rule for generating such concept generalizations. Finally, we prove that unless NP E non-uniform P, not all theories have small Horn least upper-bound approximations.


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