Logic Tensor Networks
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Samy Badreddine and Artur d'Avila Garcez and Luciano Serafini and Michael Spranger
2020
Abstract
Artificial Intelligence agents are required to learn from their surroundings
and to reason about the knowledge that has been learned in order to make
decisions. While state-of-the-art learning from data typically uses
sub-symbolic distributed representations, reasoning is normally useful at a
higher level of abstraction with the use of a first-order logic language for
knowledge representation. As a result, attempts at combining symbolic AI and
neural computation into neural-symbolic systems have been on the increase. In
this paper, we present Logic Tensor Networks (LTN), a neurosymbolic formalism
and computational model that supports learning and reasoning through the
introduction of a many-valued, end-to-end differentiable first-order logic
called Real Logic as a representation language for deep learning. We show that
LTN provides a uniform language for the specification and the computation of
several AI tasks such as data clustering, multi-label classification,
relational learning, query answering, semi-supervised learning, regression and
embedding learning. We implement and illustrate each of the above tasks with a
number of simple explanatory examples using TensorFlow 2. Keywords:
Neurosymbolic AI, Deep Learning and Reasoning, Many-valued Logic.
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2012.13635v1
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