论文标题
迈向数据和知识驱动的人工智能：神经符号计算的调查
Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing
论文作者
论文摘要
多年来一直是人工智能研究领域（AI）的积极研究领域，它追求符号和统计学范式的整合，它一直是人工智能（AI）多年。正如Nesy所表明的希望在神经网络中符合符号表示和强大学习的推理和解释性优势的希望一样，它可以作为下一代AI的催化剂。在本文中，我们对NESY研究的最新发展和重要贡献提供了系统的概述。首先，我们介绍了该领域的研究历史，涵盖了早期工作和基础。我们进一步讨论背景概念并确定NESY发展背后的关键驱动因素。之后，我们将最新的地标方法归类为强调该研究范式的几种主要特征，包括神经符号融合，知识表示，知识嵌入和功能。接下来，我们简要讨论现代NESY方法在几个领域的成功应用。然后，我们在三个代表性应用程序任务上基准了几种NESY方法。最后，我们确定了开放问题以及潜在的未来研究方向。预计这项调查将帮助新研究人员进入这个迅速发展的领域，并加速以数据和知识驱动的AI的进步。
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 recent developments and important contributions of NeSy research. 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. Next, we briefly discuss the successful application of modern NeSy approaches in several domains. Then, we benchmark several NeSy methods on three representative application tasks. Finally, we identify the open problems together with potential future research directions. This survey is expected to help new researchers enter this rapidly evolving field and accelerate the progress towards data-and knowledge-driven AI.
