ts for Fast Inference
Henry Kautz, Bart Selman · 1992
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 al-low for a more compact representation of the tractable theory. We also give a general induction rule for gen-erating such concept generalizations. Finally, we prove that unless NP E non-uniform P, not all theories have small Horn least upper-bound approximations.