论文标题
贝叶斯满足和预测：具有非单调性，支持性和预测精度的常识性推理
Bayes Meets Entailment and Prediction: Commonsense Reasoning with Non-monotonicity, Paraconsistency and Predictive Accuracy
论文作者
论文摘要
贝叶斯方法在神经科学和人工智能中的最新成功导致了大脑是贝叶斯机器的假设。由于逻辑和学习都是人脑的实践，因此导致了另一个假设，即在逻辑推理和机器学习方面存在贝叶斯的解释。在本文中，我们介绍了逻辑后果关系的生成模型。它形成了句子的真实价值是如何从世界各州的概率分布中产生的。我们表明，生成模型表征了经典的后果关系，旁sance依的后果关系和非单调后果关系。特别是，生成模型给出了一种新的后果关系，以不一致的知识在推理方面胜过它们。我们还表明，生成模型给出了一种新的分类算法，该算法在Kaggle Titanic数据集的预测精度和复杂性方面优于几种代表性算法。
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.
