The complexity and generality of learning answer set programs

This paper investigates the theoretical properties of these existing frameworks for learning programs under the answer set semantics and introduces a new notion of generality of a learning framework, which enables a framework to be more general than another in terms of being able to distinguish one ASP hypothesis solution from a set of incorrect ASP programs.