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. Furthermore, we provide a theoretical framework capturing the class of higher-order definitions handled by our extension.
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。 现有的高阶化的ILP系统显示精确度和学习成绩有所改善,尽管仍然受到基本学习机制局限性的阻碍。 实验结果表明,我们通过更阶级定义扩展多功能的“从失败中学习”范式,大大改善了学习业绩,而没有现有系统要求的烦琐的人类指导。 此外,我们提供了一个理论框架,记录我们扩展时处理的更高阶梯定义类别。