Learning complex programs through inductive logic programming (ILP) remains a formidable challenge. Existing higher-order enabled ILP systems show improved accuracy and learning performance, though remain hampered by the limitations of the underlying learning mechanism. Experimental results show that our extension of the versatile Learning From Failures paradigm by higher-order definitions significantly improves learning performance without the burdensome human guidance required by existing systems. Our theoretical framework captures a class of higher-order definitions preserving soundness of existing subsumption-based pruning methods.
翻译:通过感性逻辑编程(ILP)的学习复杂程序仍然是一项艰巨的挑战。 现有的更高层次的ILP系统显示精确度和学习成绩的提高,尽管仍然受到基本学习机制局限性的阻碍。 实验结果显示,我们通过更高层次的定义扩展多功能的“从失败中学习”模式极大地改善了学习业绩,而没有现有系统要求的烦琐的人类指导。 我们的理论框架捕捉了一类高层次的定义,保留了基于子吸收的现有修剪方法的健全性。