William W. Cohen A crucial problem in "inductive logic
programming" is learning recursive logic programs from examples
alone; current systems such as GOLEM and FOIL often achieve
success only for carefully selected sets of examples. We
describe a program called FORCE2 that uses the new technique of
"forced simulation" to learn two-clause "closed" linear
recursive ij-determinate programs; although this class of
programs is fairly restricted, it does include most of the
standard benchmark problems. Experimentally, FORCE2 requires
fewer examples than FOIL, and is more accurate when learning
from randomly chosen datasets. Formally, FORCE2 is also shown to
be a pat-learning algorithm in a variant of Valiant’s  model, in
which we assume the ability to make two types of queries: one
which gives an upper bound on the depth of the proof for an
example, and one which determines if an example can be proved in
unit depth.