Neural-symbolic computing (NeSy), which pursues the integration of the
symbolic and statistical paradigms of cognition, has been an active research
area of Artificial Intelligence (AI) for many years. As NeSy shows promise of
reconciling the advantages of reasoning and interpretability of symbolic
representation and robust learning in neural networks, it may serve as a
catalyst for the next generation of AI. In the present paper, we provide a
systematic overview of the important and recent developments of research on
NeSy AI. Firstly, we introduce study history of this area, covering early work
and foundations. We further discuss background concepts and identify key
driving factors behind the development of NeSy. Afterward, we categorize recent
landmark approaches along several main characteristics that underline this
research paradigm, including neural-symbolic integration, knowledge
representation, knowledge embedding, and functionality. Then, we briefly
discuss the successful application of modern NeSy approaches in several
domains. Finally, we identify the open problems together with potential future
research directions. This survey is expected to help new researchers enter this
rapidly-developing field and accelerate progress towards data-and
knowledge-driven AI.

本文介绍了神经符号计算（NeSy）领域的重要和最新研究发展，包括研究历史，背景知识，关键驱动因素以及主要特征。文章讨论了该领域的一些重要应用并针对目前存在的问题提出了新的研究方向，以期进一步推动以数据和知识为驱动的 AI 的研究进展。