Non-monotonic learning
Non-monotonic learning
复制标题
非单调学习
DOI：
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发表时间：
1991
期刊：
影响因子：
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通讯作者：
S. Muggleton
中科院分区：
文献类型：
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作者：
Michael Bain;S. Muggleton
本文讨论了在增量学习系统的背景下专门化一阶理论的方法。我们证明了现有一阶增量学习系统在专业化机制方面的缺点。我们证明这些缺点是经典逻辑使用的基础。特别是，在这个框架内并不总是可以获得最小的纠正“专业化。我们建议采用基于现有非单调逻辑形式主义的专业化方案。这种方法克服了采用经典逻辑的增量学习系统出现的问题。作为为本文开发的形式证明的副作用，我们定义了一个名为“deriv”的函数，它被证明是对现有的基于解释的泛化（EBG）算法的改进。附录中描述了 Prolog 代码以及 deriv" 和之前的 EBG 算法之间关系的描述。
This paper addresses methods of specialising rst-order theories within the context of incremental learning systems. We demonstrate the shortcomings of existing rst-order incremental learning systems with regard to their specialisation mechanisms. We prove that these shortcomings are fundamental to the use of classical logic. In particular, minimal correct-ing" specialisations are not always obtainable within this framework. We propose instead the adoption of a specialisation scheme based on an existing non-monotonic logic formalism. This approach overcomes the problems that arise with incremental learning systems which employ classical logic. As a side-eeect of the formal proofs developed for this paper we deene a function called deriv" which turns out to be an improvement on an existing explanation-based-generalisation (EBG) algorithm. Prolog code and a description of the relationship between deriv" and the previous EBG algorithm are described in an appendix.