神经符号学及其应用研究

Study on NeuroSymbolic learning and its applications

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。

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.