Current advances in Artificial Intelligence (AI) and Machine Learning (ML)
have achieved unprecedented impact across research communities and industry.
Nevertheless, concerns about trust, safety, interpretability and accountability
of AI were raised by influential thinkers. Many have identified the need for
well-founded knowledge representation and reasoning to be integrated with deep
learning and for sound explainability. Neural-symbolic computing has been an
active area of research for many years seeking to bring together robust
learning in neural networks with reasoning and explainability via symbolic
representations for network models. In this paper, we relate recent and early
research results in neurosymbolic AI with the objective of identifying the key
ingredients of the next wave of AI systems. We focus on research that
integrates in a principled way neural network-based learning with symbolic
knowledge representation and logical reasoning. The insights provided by 20
years of neural-symbolic computing are shown to shed new light onto the
increasingly prominent role of trust, safety, interpretability and
accountability of AI. We also identify promising directions and challenges for
the next decade of AI research from the perspective of neural-symbolic systems.

本文探讨了神经符号计算技术中深度学习和知识表示应该如何有机结合，通过 20 年来的相关研究结果，为下一个 AI 系统的关键性成分做出探索，以神经符号系统的视角提出 AI 技术应该在信任、安全、可解释性和问责制方面有所提升和解决的方向和挑战。