数据驱动与知识引导结合下人工智能算法模型

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。

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

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.