  Significant research has been conducted in recent years to extend Inductive
Logic Programming (ILP) methods to induce Answer Set Programs (ASP). These
methods perform an exhaustive search for the correct hypothesis by encoding an
ILP problem instance as an ASP program. Exhaustive search, however, results in
loss of scalability. In addition, the language bias employed in these methods
is overly restrictive too. In this paper we extend our previous work on
learning stratified answer set programs that have a single stable model to
learning arbitrary (i.e., non-stratified) ones with multiple stable models. Our
extended algorithm is a greedy FOIL-like algorithm, capable of inducing
non-monotonic logic programs, examples of which includes programs for
combinatorial problems such as graph-coloring and N-queens. To the best of our
knowledge, this is the first heuristic-based ILP algorithm to induce answer set
programs with multiple stable models.
