论文标题
通过发现不搜索的地方学习逻辑程序
Learning logic programs by discovering where not to search
论文作者
论文摘要
归纳逻辑编程（ILP）的目的是搜索概括培训示例和背景知识（BK）的假设。为了提高性能，我们介绍了一种方法，该方法在搜索假设之前，首先发现不搜索的地方。我们使用给定的BK来发现对假设的限制，例如一个数字不能均匀且奇怪。我们使用约束来引导约束驱动的ILP系统。我们在多个领域（包括程序合成和游戏玩法）上进行的实验表明，我们的方法可以（i）将学习时间大大减少97％，以及（ii）扩展到具有数百万事实的域。
The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce an approach that, before searching for a hypothesis, first discovers where not to search. We use given BK to discover constraints on hypotheses, such as that a number cannot be both even and odd. We use the constraints to bootstrap a constraint-driven ILP system. Our experiments on multiple domains (including program synthesis and game playing) show that our approach can (i) substantially reduce learning times by up to 97%, and (ii) scale to domains with millions of facts.
