Bayes满足蕴涵与预测：具有非单调性、次协调性和预测精度的常识推理
Bayes Meets Entailment and Prediction: Commonsense Reasoning with Non-monotonicity, Paraconsistency and Predictive Accuracy
摘要：
最近贝叶斯方法在神经科学和人工智能中取得的成功提出了大脑是贝叶斯机器的假设。由于逻辑和学习都是人类大脑的实践，这导致了另一个假设，即逻辑推理和机器学习都有贝叶斯解释。在本文中，我们介绍了一个生成模型的逻辑后果关系。它形式化了一个句子的真值是如何从世界状态的概率分布中概率地生成的过程。我们表明，生成模型的特点是经典的后果关系，次协调的后果关系和非单调的后果关系。特别是，生成模型给出了一个新的结果关系，优于他们的推理不一致的知识。我们还表明，生成模型给出了一种新的分类算法，在Kaggle Titanic数据集上的预测准确性和复杂性方面优于几种代表性算法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
