Iterative Learning of Answer Set Programs from Context Dependent Examples
Iterative Learning of Answer Set Programs from Context Dependent Examples
复制标题
从上下文相关示例迭代学习答案集程序
DOI：
10.1017/s1471068416000351
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发表时间：
2016
影响因子：
1.4
通讯作者：
LAW M
中科院分区：
文献类型：
--
作者：
LAW M
近年来，已经提出了一些框架和系统，将归纳逻辑编程（ILP）扩展到答案集编程（ASP）范式。在ILP中，所有的例子都必须用假设和给定的背景知识来解释。在现有系统中，所有示例的背景知识都是相同的；但是，示例可能依赖于上下文。这意味着一些例子应该在一些信息的上下文中解释，而另一些例子应该在不同的上下文中解释。在本文中，我们抓住了这个概念，并提出了从有序答案集学习框架的上下文相关扩展。在这个扩展中，上下文可以用来进一步构建背景知识。然后，我们提出了一种新的迭代算法ILASP2i，该算法利用这一特征将现有的ILASP2系统扩展到具有大量示例的学习任务。我们通过将这两种算法应用于各种学习任务来演示可扩展性的增益。结果表明，与ILASP2相比，新提出的ILASP2i系统可以在保持相同的平均精度的同时，提高两个数量级的速度，减少两个数量级的内存使用。
In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples; however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered Answer Sets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy.