论文标题
从具有部分解释的神经网络中提取规则
Extracting Rules from Neural Networks with Partial Interpretations
论文作者
论文摘要
我们研究了从Horn Logic，来自神经网络模型中提取规则提取规则的问题。我们的工作基于确切的学习模型，在该模型中，学习者通过查询与教师（神经网络模型）进行互动，以学习抽象目标概念，在我们的情况下，这是一组Horn规则。我们考虑部分解释以制定查询。这些可以理解为对世界的代表，其中一部分关于命题真实性的知识是未知的。我们采用Angluin的算法通过查询学习角规则，并经验评估我们的策略。
We investigate the problem of extracting rules, expressed in Horn logic, from neural network models. Our work is based on the exact learning model, in which a learner interacts with a teacher (the neural network model) via queries in order to learn an abstract target concept, which in our case is a set of Horn rules. We consider partial interpretations to formulate the queries. These can be understood as a representation of the world where part of the knowledge regarding the truthiness of propositions is unknown. We employ Angluin s algorithm for learning Horn rules via queries and evaluate our strategy empirically.
