The field of Statistical Relational Learning (SRL) is concerned with learning
probabilistic models from relational data. Learned SRL models are typically
represented using some kind of weighted logical formulas, which make them
considerably more interpretable than those obtained by e.g. neural networks. In
practice, however, these models are often still difficult to interpret
correctly, as they can contain many formulas that interact in non-trivial ways
and weights do not always have an intuitive meaning. To address this, we
propose a new SRL method which uses possibilistic logic to encode relational
models. Learned models are then essentially stratified classical theories,
which explicitly encode what can be derived with a given level of certainty.
Compared to Markov Logic Networks (MLNs), our method is faster and produces
considerably more interpretable models.

提出了一种使用可能性逻辑来编码关系模型的统计关系学习方法，该方法相对于马尔可夫逻辑网络更快且产生的模型更容易解释。

从关联数据中诱导可解释的可能逻辑理论

Induction of Interpretable Possibilistic Logic Theories from Relational  Data

Possibilistic answer set programming (PASP) extends answer set programming
(ASP) by attaching to each rule a degree of certainty. While such an extension
is important from an application point of view, existing semantics are not
well-motivated, and do not always yield intuitive results. To develop a more
suitable semantics, we first introduce a characterization of answer sets of
classical ASP programs in terms of possibilistic logic where an ASP program
specifies a set of constraints on possibility distributions. This
characterization is then naturally generalized to define answer sets of PASP
programs. We furthermore provide a syntactic counterpart, leading to a
possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we
show how our framework can readily be implemented using standard ASP solvers.

本文介绍了一种基于可能逻辑的新 ASP 模型，称为 Possibilistic answer set programming（PASP）。通过将对 PASP 程序中的规则的确定性附加到每个规则上，我们提出了一种更加合适的 PASP 语义，并且该框架可以轻松使用标准 ASP 求解器实现。