Lazy Explanation-Based Approximation for Probabilistic Logic Programming
  We introduce a lazy approach to the explanation-based approximation of
probabilistic logic programs. It uses only the most significant part of the
program when searching for explanations. The result is a fast and anytime
approximate inference algorithm which returns hard lower and upper bounds on
the exact probability. We experimentally show that this method outperforms
state-of-the-art approximate inference.
