从常识推理到多元偏好下的神经网络模型
From Common Sense Reasoning to Neural Network Models through Multiple Preferences: an overview
摘要：
在本文中，我们讨论了条件和偏好逻辑和神经网络模型之间的关系，基于多偏好语义。我们提出了一个概念明智的多偏好语义，最近推出的可废止的描述逻辑，考虑到不同的概念的偏好，作为一种工具，提供一个语义解释神经网络模型。这种方法已经被探索用于无监督的神经网络模型（自组织映射）和有监督的模型（多层感知器），我们希望相同的方法可以扩展到其他神经网络模型。它允许通过捕获网络的输入-输出行为的解释来检查网络的逻辑属性（通过模型检验）。对于多层感知器，深度网络本身可以被视为条件知识库，其中突触连接对应于加权条件。本文描述了一般的方法，通过自组织地图和多层感知器的情况下，并讨论了一些开放的问题和观点。
英文摘要：
In this paper we discuss the relationships between conditional and preferential logics and neural network models, based on a multi-preferential semantics. We propose a concept-wise multipreference semantics, recently introduced for defeasible description logics to take into account preferences with respect to different concepts, as a tool for providing a semantic interpretation to neural network models. This approach has been explored both for unsupervised neural network models (Self-Organising Maps) and for supervised ones (Multilayer Perceptrons), and we expect that the same approach might be extended to other neural network models. It allows for logical properties of the network to be checked (by model checking) over an interpretation capturing the input-output behavior of the network. For Multilayer Perceptrons, the deep network itself can be regarded as a conditional knowledge base, in which synaptic connections correspond to weighted conditionals. The paper describes the general approach, through the cases of Self-Organising Maps and Multilayer Perceptrons, and discusses some open issues and perspectives.
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。
