# LYRICS: a General Interface Layer to Integrate Logic Inference and Deep Learning

## TL;DR

LYRICS is a versatile interface layer that integrates first-order logic reasoning with deep learning in TensorFlow, enabling complex symbolic inference alongside data-driven learning.

## Contribution

It introduces a general framework for combining logical constraints with deep learning models, allowing flexible integration without restrictions on models or knowledge types.

## Key findings

- Supports various use cases including model checking, supervised learning, and collective classification.
- Enables learning weights of models under logical constraints.
- Provides a unified language for defining and binding logical knowledge to deep learning models.

## Abstract

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.