论文标题
贝叶斯综合假设：大脑如何实施单调和非单调推理
Bayesian Entailment Hypothesis: How Brains Implement Monotonic and Non-monotonic Reasoning
论文作者
论文摘要
贝叶斯方法在神经科学和人工智能方面的最新成功导致了大脑是贝叶斯机器的假设。由于逻辑作为思想定律，是人脑的产物和实践，因此导致了另一个假设，即存在贝叶斯算法和逻辑推理的数据结构。在本文中，我们给出了贝叶斯的叙述，并描述了其抽象的推论特性。在极端情况下，贝叶斯的元素被证明是一种单调的后果关系。通常，这是一种非单调的后果关系，而没有谨慎的单调或切割。优先组合是代表性的非单调后果关系，被证明是最大的后验，这是贝叶斯的近似值。最终，我们讨论了我们的建议的优点，以编码对默认值，处理变化和矛盾以及建模人类的偏好。
Recent success of Bayesian methods in neuroscience and artificial intelligence gives rise to the hypothesis that the brain is a Bayesian machine. Since logic, as the laws of thought, is a product and practice of the human brain, it leads to another hypothesis that there is a Bayesian algorithm and data-structure for logical reasoning. In this paper, we give a Bayesian account of entailment and characterize its abstract inferential properties. The Bayesian entailment is shown to be a monotonic consequence relation in an extreme case. In general, it is a sort of non-monotonic consequence relation without Cautious monotony or Cut. The preferential entailment, which is a representative non-monotonic consequence relation, is shown to be maximum a posteriori entailment, which is an approximation of the Bayesian entailment. We finally discuss merits of our proposals in terms of encoding preferences on defaults, handling change and contradiction, and modeling human entailment.
