Learning Possibilistic Logic Theories from Default Rules
  We introduce a setting for learning possibilistic logic theories from
defaults of the form "if alpha then typically beta". We first analyse this
problem from the point of view of machine learning theory, determining the VC
dimension of possibilistic stratifications as well as the complexity of the
associated learning problems, after which we present a heuristic learning
algorithm that can easily scale to thousands of defaults. An important property
of our approach is that it is inherently able to handle noisy and conflicting
sets of defaults. Among others, this allows us to learn possibilistic logic
theories from crowdsourced data and to approximate propositional Markov logic
networks using heuristic MAP solvers. We present experimental results that
demonstrate the effectiveness of this approach.
