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We introduce a setting for learning possibilistic logic theories fromdefaults of the form "if alpha then typically beta". We first analyse thisproblem from the point of view of machine learning theory, determining the VCdimension of possibilistic stratifications as well as the complexity of theassociated learning problems, after which we present a heuristic learningalgorithm that can easily scale to thousands of defaults. An important propertyof our approach is that it is inherently able to handle noisy and conflictingsets of defaults. Among others, this allows us to learn possibilistic logictheories from crowdsourced data and to approximate propositional Markov logicnetworks using heuristic MAP solvers. We present experimental results thatdemonstrate the effectiveness of this approach.

Learning Possibilistic Logic Theories from Default Rules