Learning and reasoning in logic tensor networks: theory and application to semantic image interpretation

L. Serafini, Ivan Donadello, A. Garcez
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引用次数: 21

Abstract

This paper presents a revision of Real Logic and its implementation with Logic Tensor Networks and its application to Semantic Image Interpretation. Real Logic is a framework where learning from numerical data and logical reasoning are integrated using first order logic syntax. The symbols of the signature of Real Logic are interpreted in the data-space, i.e, on the domain of real numbers. The integration of learning and reasoning obtained in Real Logic allows us to formalize learning as approximate satisfiability in the presence of logical constraints, and to perform inference on symbolic and numerical data. After introducing a refined version of the formalism, we describe its implementation into Logic Tensor Networks which uses deep learning within Google's TensorFlow™. We evaluate LTN on the task of classifying objects and their parts in images, where we combine state-of-the-art-object detectors with a part-of ontology. LTN outperforms the state-of-the-art on object classification, and improves the performances on part-of relation detection with respect to a rule-based baseline.
逻辑张量网络的学习与推理:理论及其在语义图像解释中的应用
本文提出了Real Logic的一个修订版及其在逻辑张量网络中的实现及其在语义图像解释中的应用。Real Logic是一个框架,其中从数字数据学习和逻辑推理是集成使用一阶逻辑语法。实逻辑的签名符号是在数据空间,即实数域上解释的。在Real Logic中获得的学习和推理的集成使我们能够将学习形式化为存在逻辑约束的近似可满足性,并对符号和数值数据进行推理。在介绍了形式主义的改进版本之后,我们将其描述为逻辑张量网络的实现,该网络使用b谷歌的TensorFlow™中的深度学习。我们评估LTN对图像中对象及其部分分类的任务,其中我们将最先进的对象检测器与部分本体相结合。LTN在对象分类方面优于最先进的技术,并且相对于基于规则的基线提高了部分关系检测的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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