数据驱动与知识引导结合下人工智能算法模型

金哲; 张引; 吴飞; 朱文武; 潘云鹤

doi:10.11999/JEIT220700

数据驱动与知识引导结合下人工智能算法模型

doi: 10.11999/JEIT220700

金哲¹,
张引^1, ,,
吴飞¹,
朱文武²,
潘云鹤¹

1.
浙江大学计算机科学与技术学院杭州 310012
2.
清华大学计算机科学与技术系北京 100084

基金项目: 中国工程科技知识中心项目(CKCEST-2021-1-8)，国家自然科学基金(62037001)

详细信息

作者简介:
金哲：男，博士生，研究方向为自然语言处理和知识图谱

张引：女，副教授，研究方向为数据挖掘、知识工程

吴飞：男，教授，研究方向为人工智能、多媒体分析

朱文武：男，教授，研究方向为多媒体网络计算、大数据智能

潘云鹤：男，教授，研究方向为人工智能、计算机图形学、智能CAD

通讯作者:
张引　yinzh@zju.edu.cn

中图分类号: TN911; TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2022-05-20
- 修回日期: 2022-08-24
- 网络出版日期: 2022-08-29
- 刊出日期: 2023-07-10

Artificial Intelligence Algorithms Based on Data-driven and Knowledge-guided Models

JIN Zhe¹,
ZHANG Yin^{1
, ,},
WU Fei¹,
ZHU Wenwu²,
PAN Yunhe¹

1.
College of Computer Science and Technology, Zhejiang University, Hangzhou 310012, China
2.
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Funds: China Knowledge Centre for Engineering Sciences and Technology Project (CKCEST-2021-1-8), The National Natural Science Foundation of China (62037001)

摘要

摘要: 当前人工智能的学习模式主要以数据驱动为主要手段，以深度神经网络为主流的机器学习算法取得了显著进展。但是这种数据驱动的人工智能手段依然面临数据获取成本高、可解释性弱、鲁棒性不强等不足。该文认为在现有机器学习算法中引入先验假设、逻辑规则和方程公式等知识，建立数据和知识双轮驱动的人工智能方法，将推动更通用计算范式的变革创新。该文将可用于引导人工智能算法模型知识归纳为4种——逻辑知识、视觉知识、物理定律知识和因果知识，探讨将这些知识与现有数据驱动模型相互结合的典型方法。
- 人工智能 /
- 大数据 /
- 数据驱动 /
- 知识引导
Abstract: Nowadays, artificial intelligence is in the era of big data-driven manner. Machine learning algorithms with deep neural networks as the mainstream have achieved great development and achievements. However, data-driven artificial intelligence still faces problems such as the cost of annotating data, the lack of interpretability, and the weak robustness. The Introduction of knowledge such as prior hypothesis, logic rules and physical equations into existing machine learning algorithms will build artificial intelligence approaches powered by both data and knowledge which could promote innovations of computing paradigm. Four types of knowledge (logical knowledge, visual knowledge, laws of physics knowledge and causal knowledge) that can be used to guide artificial intelligence algorithm models are summarized in thus paper, and typical approaches to guide the combination of these knowledge with data-driven models are discussed.
- Artificial intelligence /
- Big data /
- Data-drive /
- Knowledge-guided

HTML全文

图 1 可与数据驱动机器学习模型相互结合的4种知识

下载: 全尺寸图片幻灯片

图 2 双轮驱动学习算法的常见模式

下载: 全尺寸图片幻灯片

表 1 知识引导的典型方法

知识	方法	具体思路	例子
逻辑知识	知识图谱表示	将知识图谱中的实体和关系表示为向量。	Bordes等人^[35]、Lin等人^[117]、Dettmers等人^[118]
逻辑知识	约束条件	使用知识作为优化的约束条件。	Hu等人^[39]、Chen等人^[40]
视觉知识	视觉知识抽取及应用	建立基于视觉知识的闭环学习机制。	Wu等人^[65]
视觉知识	视觉知识抽取及应用	构建目标概念的视觉知识字典。	Pu等人^[62]
科学定律知识	偏微分方程求解	流体力学中将不可压纳维-斯托克斯方程组与神经网络损失函数结合。	Raissi等人^[76]、Jin等人^[119]
		生物医学中基于PINN求解心脏激活映射、心血管动脉压力相关函数。	Sahli Costabal等人^[78]、Kissas等人^[120]
		材料领域中基于PINN解决频域麦克斯韦方程组和超材料设计问题、连续体固体力学的几何识别问题。	Fang等人^[79]、Zhang等人^[80]
		电力学中基于PINN求解电力系统动力学中的摆动方程。	Misyris等人^[121]
		使用神经算子和深度算子网络求解。	Li等人^[81]、Lu等人^[82]
	组合优化问题求解	基于自旋哈密顿函数、2元无约束优化等形式训练图神经网络的可微损失函数。	Schuetz等人^[122]
	先验知识与约束条件	蛋白质结构预测中结合蛋白质先验结构、氨基酸链的物理特性约束等知识。	Jumper等人^[85]、Baek等人^[87]、Humphreys等人^[88]
		逆合成分析中使用化学反应过程中变化的原子和键构建转化规则集，用于后续蒙特卡罗树搜索。	Segler等人^[123]
		晶体结构预测中建立晶体结构和生成焓之间的关联模型。	Cheng等人^[124]
		地表温度反演中使用辐射传递方程作为反演机制的数学推导。	Wang等人^[125]
		胸部X射线检查中使用基于X射线报告中的知识进行驱动的推理算法提高深度学习模型性能。	Jadhav等人^[126]
因果知识	引入因果关系	尝试将因果关系引入机器学习模型中。	Kuang等人^[45]、Kuang等人^[46]

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