A Novel Neural-symbolic System under Statistical Relational Learning
Dongran Yu Xueyan Liu Shirui Pan Anchen Li Bo Yang
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.