Induction from answer sets in nonmonotonic logic programs
Induction from answer sets in nonmonotonic logic programs
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DOI：
10.1145/1055686.1055687
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发表时间：
2005-04
期刊：
影响因子：
--
通讯作者：
Chiaki Sakama
中科院分区：
文献类型：
--
作者：
Chiaki Sakama
归纳逻辑编程(ILP)实现了计算逻辑中的归纳机器学习。然而，目前的ILP主要处理经典的子句程序，尤其是Horn逻辑程序，在学习非单调逻辑程序方面的应用有限。研究了在非单调逻辑程序中实现归纳法的一种方法。我们以扩展的逻辑程序为背景理论，介绍了利用程序的答案集生成新规则的技术。产生的新规则在归纳逻辑编程的背景下解释了正/负示例。所提出的方法将现有的ILP技术扩展到更丰富的句法和语义框架，并为非单调ILP理论做出了贡献。
Inductive logic programming (ILP) realizes inductive machine learning in computational logic. However, the present ILP mostly handles classical clausal programs, especially Horn logic programs, and has limited applications to learning nonmonotonic logic programs. This article studies a method for realizing induction in nonmonotonic logic programs. We consider an extended logic program as a background theory, and introduce techniques for inducing new rules using answer sets of the program. The produced new rules explain positive/negative examples in the context of inductive logic programming. The proposed methods extend the present ILP techniques to a syntactically and semantically richer framework, and contribute to a theory of nonmonotonic ILP.