神经符号学及其应用研究

doi:10.11959/j.issn.2096-6652.202234

Abstract

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.

Key words: AI, NeuroSymbolic learning, robot motion planning, robot task planning, video evaluation for educational experiment

CLC Number:

TP242.6

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.

Figures/Tables 9

References 33

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