>> ASP has both classical negation and negation-as-failure, but that is not quite why I'd say it is "more declarative". Prolog is capable of performing "imperative" tasks (by this I really mean side effects), while ASP solvers exclusively find stable models (answer sets) that may (or may not) exist for a given logic program. In ASP you can only declare a model with your input, and the output is either one or more stable model(s), or the knowledge that the model was unsatisfiable.

Thanks for the insight. I must confess I still don't have a lot of experience with ASP, mainly because of my initial misunderstanding of it.

To clarify, I'm not interested in the declarative aspect of logic programming so Prolog's side-effect-ness doesn't bother me. I prefer it in fact that Prolog is pragmatic that way and allows itself to be used to do practical work, that would otherwise have to be delegated to another language or tool. Prolog is a big, dirty ball of cheating but I've kind of made my home in it and I'm comfortable there.

But my research interest is in machine learning of logic programs (Inductive Logic Programming, ILP). A big part of that is dealing with noise and uncertainty, which traditional approaches to ILP aren't very good at. In recent years there has been a flurry of work in learning either ASP, or with ASP, and I guess I feel a bit like an idiot to finally realise why. My hope now is that I can find a way to reuse the ideas in ASP with the learning framework I studied in my PhD, where first-order programs are learned by a form of higher-order SLD-Resolution. I think the combination of a sound and refutation-complete inductive algorithm with an elegant treatment of uncertainty could produce something really unique.