Weight Learning in a Probabilistic Extension of Answer Set Programs
  LPMLN is a probabilistic extension of answer set programs with the weight
scheme derived from that of Markov Logic. Previous work has shown how inference
in LPMLN can be achieved. In this paper, we present the concept of weight
learning in LPMLN and learning algorithms for LPMLN derived from those for
Markov Logic. We also present a prototype implementation that uses answer set
solvers for learning as well as some example domains that illustrate distinct
features of LPMLN learning. Learning in LPMLN is in accordance with the stable
model semantics, thereby it learns parameters for probabilistic extensions of
knowledge-rich domains where answer set programming has shown to be useful but
limited to the deterministic case, such as reachability analysis and reasoning
about actions in dynamic domains. We also apply the method to learn the
parameters for probabilistic abductive reasoning about actions.
