The recent success of Bayesian methods in neuroscience and artificial
intelligence gives rise to the hypothesis that the brain is a Bayesian machine.
Since logic and learning are both practices of the human brain, it leads to
another hypothesis that there is a Bayesian interpretation underlying both
logical reasoning and machine learning. In this paper, we introduce a
generative model of logical consequence relations. It formalises the process of
how the truth value of a sentence is probabilistically generated from the
probability distribution over states of the world. We show that the generative
model characterises a classical consequence relation, paraconsistent
consequence relation and nonmonotonic consequence relation. In particular, the
generative model gives a new consequence relation that outperforms them in
reasoning with inconsistent knowledge. We also show that the generative model
gives a new classification algorithm that outperforms several representative
algorithms in predictive accuracy and complexity on the Kaggle Titanic dataset.

本文提出一种基于生成模型的逻辑推理关系，将真值的概率生成过程化，证明了该模型优于其他推理模型，同时给出了一种性能优越的分类算法。