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.

从机器学习角度分析了默认形式“如果alpha通常是beta”学习可能主义逻辑理论的问题，然后提出了一种启发式学习算法，可以轻松扩展到数千个默认项，并且能够处理嘈杂和冲突的默认集合。通过使用该方法，我们可以从众包数据中学习可能主义逻辑理论，并使用启发式MAP求解程序来近似命题Markov逻辑网络。实验结果表明了该方法的有效性。

通过默认规则学习Possibilistic逻辑理论