Inductive logic programming (ILP) is a framework of learning logic programs from examples and background knowledge. In some real-world applications, we are particularly interested in learning aspects of system dynamics that are characterized by state transitions for which logic programs are shown to be expressive. In this work, we study this particular form of ILP—learning logic programs from state/interpretation transitions. Firstly, we define a state transition operator for disjunctive logic programs which generalizes the immediate consequence operator for normal logic programs. Secondly, we formulate two resolutions to simplify logic programs under state transition and study their properties. Finally, we put forward an inductive learning framework, which is shown to provide a sound and complete procedure for learning disjunctive logic programs from state transitions. A prototype system is implemented in Python and evaluated with randomly generated examples of well-known Boolean network benchmarks.

中文翻译：

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从非确定性解释转换中学习析取逻辑程序

归纳逻辑编程 (ILP) 是从示例和背景知识中学习逻辑程序的框架。在一些现实世界的应用中，我们对系统动力学的学习方面特别感兴趣，这些方面的特点是状态转换，逻辑程序显示出具有表现力的状态转换。在这项工作中，我们研究了这种特殊形式的 ILP——从状态/解释转换中学习逻辑程序。首先，我们为析取逻辑程序定义了一个状态转换算子，它概括了正常逻辑程序的直接后果算子。其次，我们制定了两个决议来简化状态转换下的逻辑程序并研究它们的性质。最后，我们提出了一个归纳学习框架，它被证明为从状态转换中学习析取逻辑程序提供了一个健全和完整的过程。一个原型系统是用 Python 实现的，并使用随机生成的著名布尔网络基准测试示例进行评估。