Learning Higher-Order Logic Programs through Abstraction and Invention
Learning Higher-Order Logic Programs through Abstraction and Invention
复制标题
通过抽象和发明学习高阶逻辑程序
DOI：
--
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
S. Muggleton
中科院分区：
文献类型：
--
作者：
Andrew Cropper;S. Muggleton
人工智能中的许多任务都需要设计复杂的程序和表示，无论是编程机器人、设计游戏程序还是进行文本或视觉转换。本文探索了一种新颖的归纳逻辑编程方法，从示例中学习此类程序。为了降低学习程序的复杂性，从而降低对此类程序的搜索，我们引入了涉及抽象和发明交替的高阶操作。抽象是使用包含高阶谓词变量的逻辑程序定义来描述的。发明涉及抽象中使用的谓词变量的定义的构造。抽象的使用扩展了元解释学习框架，并通过用户可扩展的高阶运算符集（例如 map、until 和 ifthenelse）的使用来支持。使用这些运算符可以降低表达目标程序类别所需的文本复杂性。我们提供的样本复杂性结果表明，该方法可以减少达到高预测准确性所需的示例数量，并显着减少总体学习时间。我们的实验表明，在所有情况下，准确性都会提高，学习时间也会减少。我们相信这篇论文是文献中第一篇展示使用高阶抽象的效率和准确性优势的论文。
Many tasks in AI require the design of complex programs and representations, whether for programming robots, designing game-playing programs, or conducting textual or visual transformations. This paper explores a novel inductive logic programming approach to learn such programs from examples. To reduce the complexity of the learned programs, and thus the search for such a program, we introduce higher-order operations involving an alternation of Abstraction and Invention. Abstractions are described using logic program definitions containing higher-order predicate variables. Inventions involve the construction of definitions for the predicate variables used in the Abstractions. The use of Abstractions extends the Meta-Interpretive Learning framework and is supported by the use of a user-extendable set of higher-order operators, such as map, until, and ifthenelse. Using these operators reduces the textual complexity required to express target classes of programs. We provide sample complexity results which indicate that the approach leads to reductions in the numbers of examples required to reach high predictive accuracy, as well as significant reductions in overall learning time. Our experiments demonstrate increased accuracy and reduced learning times in all cases. We believe that this paper is the first in the literature to demonstrate the efficiency and accuracy advantages involved in the use of higher-order abstractions.