Inductive Logic Programming as Abductive Search
Inductive Logic Programming as Abductive Search
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作为归纳搜索的归纳逻辑编程
DOI：
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发表时间：
2010
期刊：
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通讯作者：
Emil C. Lupu
中科院分区：
文献类型：
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作者：
D. Corapi;A. Russo;Emil C. Lupu
本文提出了一种新的非单调ILP方法及其实现，称为TAL（Top-directed Abductive Learning）。TAL克服了基于逆蕴涵的ILP系统的一些完备性问题，是第一个自顶向下的ILP系统，允许背景理论和假设是正常的逻辑程序。该方法依赖于映射到一个等价的ALP一个ILP问题。这使得使用既定的ALP证明程序和规范更丰富的语言偏见与完整性约束。该映射为ILP问题提供了一个有原则的搜索空间，在该空间上使用溯因搜索来计算归纳解。
We present a novel approach to non-monotonic ILP and its implementation called TAL (Top-directed Abductive Learning). TAL overcomes some of the completeness problems of ILP systems based on Inverse Entailment and is the first top-down ILP system that allows background theories and hypotheses to be normal logic programs. The approach relies on mapping an ILP problem into an equivalent ALP one. This enables the use of established ALP proof procedures and the specification of richer language bias with integrity constraints. The mapping provides a principled search space for an ILP problem, over which an abductive search is used to compute inductive solutions.