Inspired by empirical work in neuroscience for Bayesian approaches to brain function, we give a unified probabilistic account of various types of symbolic reasoning from data. We characterise them in terms of formal logic using the classical consequence relation, an empirical consequence relation, maximal consistent sets, maximal possible sets and maximum likelihood estimation. The theory gives new insights into reasoning towards human-like machine intelligence.

翻译：受神经科学中关于贝叶斯方法解释脑功能的实证研究启发，我们提出了一个基于概率的统一框架，用以描述从数据中进行的各类符号推理。我们通过形式逻辑中的经典推论关系、经验推论关系、极大一致集、极大可能集以及极大似然估计对这些推理进行刻画。该理论为推进类人机器智能中的推理研究提供了新见解。