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 and background concepts of this area. Afterward, we categorize recent approaches along several main characteristics that underline this research paradigm, including neural-symbolic interrelation, neural architecture, knowledge representation, 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.
翻译:神经-精神计算(Nesy-Sy)力求综合认知的象征和统计范式,多年来一直是人工智能(AI)的一个积极研究领域,由于Nesy-Sy在神经网络中展现了调和象征性代表性和强力学习的推理和可解释性的优势的前景,它可以成为下一代AI的催化剂。在本文件中,我们系统地概述了Nesy AI研究的重要和近期动态。首先,我们介绍了该领域的历史和背景概念。随后,我们按照强调这一研究范式的若干主要特征,将最近的做法分类,包括神经-精神-精神-互动、神经结构、知识代表性和功能。然后,我们简要地讨论了现代神经-精神方法在若干领域的成功应用。最后,我们找出了开放的问题和潜在的未来研究方向。