  In recent years, neural systems have demonstrated highly effective learning
ability and superior perception intelligence. However, they have been found to
lack effective reasoning and cognitive ability. On the other hand, symbolic
systems exhibit exceptional cognitive intelligence but suffer from poor
learning capabilities when compared to neural systems. Recognizing the
advantages and disadvantages of both methodologies, an ideal solution emerges:
combining neural systems and symbolic systems to create neural-symbolic
learning systems that possess powerful perception and cognition. The purpose of
this paper is to survey the advancements in neural-symbolic learning systems
from four distinct perspectives: challenges, methods, applications, and future
directions. By doing so, this research aims to propel this emerging field
forward, offering researchers a comprehensive and holistic overview. This
overview will not only highlight the current state-of-the-art but also identify
promising avenues for future research.
