论文标题
通过超越需要学习大型逻辑程序
Learning large logic programs by going beyond entailment
论文作者
论文摘要
归纳逻辑编程（ILP）的主要挑战是学习大型程序。我们认为，现有系统的关键局限性是它们使用需要指导假设搜索。这种方法是有限的，因为核心是二进制决策：假设要么需要一个例子，要么不这样做，并且没有中间位置。为了解决这一限制，我们超越了需要，并使用\ emph {示例依赖性}损失函数来指导搜索，在该搜索中，假设可以部分介绍一个示例。我们在Brute中实现了我们的想法，这是一种新的ILP系统，它使用最佳优先搜索，以示例依赖性损失函数为指导，以逐步构建程序。我们对三个不同程序合成域（机器人计划，弦变换和ASCII ART）进行的实验表明，在预测精度和学习时间方面，Brute可以基本上优于现有的ILP系统，并且可以学习比最新系统大20倍的程序。
A major challenge in inductive logic programming (ILP) is learning large programs. We argue that a key limitation of existing systems is that they use entailment to guide the hypothesis search. This approach is limited because entailment is a binary decision: a hypothesis either entails an example or does not, and there is no intermediate position. To address this limitation, we go beyond entailment and use \emph{example-dependent} loss functions to guide the search, where a hypothesis can partially cover an example. We implement our idea in Brute, a new ILP system which uses best-first search, guided by an example-dependent loss function, to incrementally build programs. Our experiments on three diverse program synthesis domains (robot planning, string transformations, and ASCII art), show that Brute can substantially outperform existing ILP systems, both in terms of predictive accuracies and learning times, and can learn programs 20 times larger than state-of-the-art systems.
