论文标题
约束驱动的多任务学习
Constraint-driven multi-task learning
论文作者
论文摘要
归纳逻辑编程是一种基于数学逻辑的机器学习形式，该数学逻辑从给定的示例和背景知识中生成逻辑程序。 在此项目中，我们扩展了Popper ILP系统以利用多任务学习。我们实施了最新方法和几种提高搜索性能的新策略。此外，我们引入了约束保存，该技术可改善所有方法的整体性能。 约束保存使系统可以在背景知识集的更新之间传输知识。因此，我们减少了系统执行的重复工作量。此外，约束保存使我们能够从当前的最新迭代加深搜索方法过渡到更有效的广度首次搜索方法。 最后，我们尝试了课程学习技术，并显示了它们对该领域的潜在好处。
Inductive logic programming is a form of machine learning based on mathematical logic that generates logic programs from given examples and background knowledge. In this project, we extend the Popper ILP system to make use of multi-task learning. We implement the state-of-the-art approach and several new strategies to improve search performance. Furthermore, we introduce constraint preservation, a technique that improves overall performance for all approaches. Constraint preservation allows the system to transfer knowledge between updates on the background knowledge set. Consequently, we reduce the amount of repeated work performed by the system. Additionally, constraint preservation allows us to transition from the current state-of-the-art iterative deepening search approach to a more efficient breadth first search approach. Finally, we experiment with curriculum learning techniques and show their potential benefit to the field.
