Related papers: A Novel Neural-symbolic System under Statistical Relational Learning

A Novel Neural-symbolic System under Statistical Relational Learning

URL: http://arxiv.org/abs/2309.08931v1
Date: Sat, 16 Sep 2023 09:15:37 GMT
Title: A Novel Neural-symbolic System under Statistical Relational Learning
Authors: Dongran Yu, Xueyan Liu, Shirui Pan, Anchen Li and Bo Yang
Abstract summary: We propose a general bi-level probabilistic graphical reasoning framework called GBPGR. In GBPGR, the results of symbolic reasoning are utilized to refine and correct the predictions made by the deep learning models. Our approach achieves high performance and exhibits effective generalization in both transductive and inductive tasks.
Score: 50.747658038910565
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: A key objective in field of artificial intelligence is to develop cognitive models that can exhibit human-like intellectual capabilities. One promising approach to achieving this is through neural-symbolic systems, which combine the strengths of deep learning and symbolic reasoning. However, current approaches in this area have been limited in their combining way, generalization and interpretability. To address these limitations, we propose a general bi-level probabilistic graphical reasoning framework called GBPGR. This framework leverages statistical relational learning to effectively integrate deep learning models and symbolic reasoning in a mutually beneficial manner. In GBPGR, the results of symbolic reasoning are utilized to refine and correct the predictions made by the deep learning models. At the same time, the deep learning models assist in enhancing the efficiency of the symbolic reasoning process. Through extensive experiments, we demonstrate that our approach achieves high performance and exhibits effective generalization in both transductive and inductive tasks.

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