  Current advances in Artificial Intelligence and machine learning in general,
and deep learning in particular have reached unprecedented impact not only
across research communities, but also over popular media channels. However,
concerns about interpretability and accountability of AI have been raised by
influential thinkers. In spite of the recent impact of AI, several works have
identified the need for principled knowledge representation and reasoning
mechanisms integrated with deep learning-based systems to provide sound and
explainable models for such systems. Neural-symbolic computing aims at
integrating, as foreseen by Valiant, two most fundamental cognitive abilities:
the ability to learn from the environment, and the ability to reason from what
has been learned. Neural-symbolic computing has been an active topic of
research for many years, reconciling the advantages of robust learning in
neural networks and reasoning and interpretability of symbolic representation.
In this paper, we survey recent accomplishments of neural-symbolic computing as
a principled methodology for integrated machine learning and reasoning. We
illustrate the effectiveness of the approach by outlining the main
characteristics of the methodology: principled integration of neural learning
with symbolic knowledge representation and reasoning allowing for the
construction of explainable AI systems. The insights provided by
neural-symbolic computing shed new light on the increasingly prominent need for
interpretable and accountable AI systems.
