  In inductive learning of a broad concept, an algorithm should be able to
distinguish concept examples from exceptions and noisy data. An approach
through recursively finding patterns in exceptions turns out to correspond to
the problem of learning default theories. Default logic is what humans employ
in common-sense reasoning. Therefore, learned default theories are better
understood by humans. In this paper, we present new algorithms to learn default
theories in the form of non-monotonic logic programs. Experiments reported in
this paper show that our algorithms are a significant improvement over
traditional approaches based on inductive logic programming.
