Abstract. We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning. A logic formalism called Real Logic is defined on a first-order language whereby formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers. Logical constants are interpreted as feature vectors of real numbers. Real Logic promotes a well-founded integration of deductive reasoning on a knowledge-base and efficient data-driven relational machine learning. We show how Real Logic can be implemented in deep Tensor Neural Networks with the use of Google's TEN-SORFLOW TM primitives. The paper concludes with experiments applying Logic Tensor Networks on a simple but representative example of knowledge completion.
Also, Serafini and Garcez REF have proposed Real Logic for the integration of learning and reasoning.
Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge
ArXiv
ArXiv
Computer Science