A Unifying Framework for Analysis and Evaluation of Inductive Programming Systems
A Unifying Framework for Analysis and Evaluation of Inductive Programming Systems
复制标题
归纳编程系统分析和评估的统一框架
DOI：
10.2991/agi.2009.16
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发表时间：
2009
影响因子：
1.1
通讯作者：
Ute Schmid
中科院分区：
文献类型：
--
作者：
M. Hofmann;E. Kitzelmann;Ute Schmid
在本文中，我们对几种归纳编程（IP）系统进行了比较。 IP 解决了从不完整的规范（例如输入/输出示例）学习（递归）程序的问题。首先，我们引入条件高阶项重写作为归纳逻辑和归纳函数程序综合的通用框架。然后，我们描述了几个 ILP 系统的特征，这些系统要么属于最近研究的，要么属于该框架内当前最强大的 IP 系统。因此，我们提出归纳功能系统 IGOR II 作为一种强大而有效的 IP 方法。对所有系统在一组代表性样本问题上的性能进行了评估，并显示了 IGOR II 的优势。
In this paper we present a comparison of several inductive programming (IP) systems. IP addresses the problem of learning (recursive) programs from incomplete specifications, such as input/output examples. First, we introduce conditional higher-order term rewriting as a common framework for inductive logic and inductive functional program synthesis. Then we characterise the several ILP systems which belong either to the most recently researched or currently to the most powerful IP systems within this framework. In consequence, we propose the inductive functional system IGOR II as a powerful and efficient approach to IP. Performance of all systems on a representative set of sample problems is evaluated and shows the strength of IGOR II.