Probabilistic Inductive Logic Programming Based on Answer Set Programming [article]

Matthias Nickles, Alessandra Mileo
2014 arXiv   pre-print
We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP). It allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities and for learning of such weights from data (parameter estimation). Weighted formulas are given a semantics in terms of soft and hard constraints which determine a probability distribution over answer sets. In contrast to related approaches, we approach inference
more » ... y optionally utilizing so-called streamlining XOR constraints, in order to reduce the number of computed answer sets. Our approach is prototypically implemented. Examples illustrate the introduced concepts and point at issues and topics for future research.
arXiv:1405.0720v1 fatcat:ahr4hjvqmzf6hdy5cj34gsfesa