Study on NeuroSymbolic learning and its applications

Yinghao CAI; Hua YANG; Xuan AN; Wenshuo WANG; Yidong DU; Jiatao ZHANG; Zhigang WANG

doi:10.11959/j.issn.2096-6652.202234

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Study on NeuroSymbolic learning and its applications

Special Column: Machine Learning Methods in AI 3.0 | 更新时间：2024-06-05

- Study on NeuroSymbolic learning and its applications
- Chinese Journal of Intelligent Science and Technology Vol. 4, Issue 4, Pages: 560-570(2022)
- 作者机构：
  
  1. 中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京 100190
  2. 中国科学院大学人工智能学院，北京 100049
  3. 英特尔中国研究院，北京 100190
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202234
  CLC： TP242.6
- Published Online：2022-12，
  
  Published：15 December 2022
- 稿件说明：
移动端阅览
Yinghao CAI, Hua YANG, Xuan AN, et al. Study on NeuroSymbolic learning and its applications[J]. Chinese journal of intelligent science and technology, 2022, 4(4): 560-570.
DOI：

Yinghao CAI, Hua YANG, Xuan AN, et al. Study on NeuroSymbolic learning and its applications[J]. Chinese journal of intelligent science and technology, 2022, 4(4): 560-570. DOI： 10.11959/j.issn.2096-6652.202234.

摘要

深度学习在感知智能上的不断突破推动了人工智能在各领域的广泛应用。但在实际落地过程中，只有把感知智能提高到更高层的认知智能，才能更好地满足日益复杂的应用需求。神经符号学将擅长感知任务的神经网络方法和擅长推理任务的逻辑符号学有机地融合在一起，是实现高层认知智能的途径之一。基于此，提出了一套神经符号学的实用框架NSFOL，并基于NSFOL实现了机器人任务规划、自学习机器人运动规划和教育实验视频评估3个典型应用。实验结果表明，尽管NSFOL尚未完善，但是它已经能够很好地支持相关应用，在可学习、可推理、可解释和可泛化方面具备一定的优势。希望通过阐述神经符号学的阶段性研究成果，激发更多的思考和研究，共同推动神经符号学的发展。

Abstract

The continuous breakthrough of deep learning in perception has promoted the application of AI in various fields.It is found that we can not meet the requirements without improving the intelligence from perception level to higher cognition level.NeuroSymbolic learning can seamlessly integrate neural network methods

that are good at perception tasks

and logical symbolic methods

that are good at reasoning tasks.Therefore

it is one of the best candidates to achieve high-level cognitive intelligence.A practical framework for NeuroSymbolic learning:NSFOL was proposed.Moreover

three typical applications based on NSFOL: robot motion planning

robot task planning and video evaluation for educational experiment were presented.Experiments show that NSFOL can support these three specific applications successfully.Moreover

these implementations have advantages in learn ability

reasonability

interpretability and generalizability.Hope to stimulate more thinking and research to jointly promote research in NeuroSymbolic learning by sharing our preliminary studies in this direction.

关键词

Keywords

references

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