In spite of the amazing results obtained by deep learning in many
applications, a real intelligent behavior of an agent acting in a complex
environment is likely to require some kind of higher-level symbolic inference.
Therefore, there is a clear need for the definition of a general and tight
integration between low-level tasks, processing sensorial data that can be
effectively elaborated using deep learning techniques, and the logic reasoning
that allows humans to take decisions in complex environments. This paper
presents LYRICS, a generic interface layer for AI, which is implemented in
TersorFlow (TF). LYRICS provides an input language that allows to define
arbitrary First Order Logic (FOL) background knowledge. The predicates and
functions of the FOL knowledge can be bound to any TF computational graph, and
the formulas are converted into a set of real-valued constraints, which
participate to the overall optimization problem. This allows to learn the
weights of the learners, under the constraints imposed by the prior knowledge.
The framework is extremely general as it imposes no restrictions in terms of
which models or knowledge can be integrated. In this paper, we show the
generality of the approach showing some use cases of the presented language,
including model checking, supervised learning and collective classification.

LYRICS 是一个使用 First Order Logic 的输入语言，它可以与任何 TersorFlow 计算图相结合，并将公式转换成一系列实值约束条件，以满足先前确定的知识并允许学习者的权重。它在各种模型和知识方面都非常通用，其中包括模型检查、监督学习和集成分类的案例分析。