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Synthesis of quantifier-free DNF sentences from inconsistent samples of strings with EF games and SAT

机译:从EF游戏和SAT的串网格上的不一致样品综合综合符号DNF句子

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摘要

We present a method to extract knowledge in terms of quantifier-free sentences in disjunctive normal form from noisy samples of classified strings. We show that the problem to find such a sentence is NP-complete, and our approach for solving it is based on a reduction to the Boolean satisfiability problem. Moreover, our method bounds the number of disjuncts and the maximum number of literals per clause since sentences with few clauses and few literals per clause are easier to interpret. As the logic we are considering defines exactly the class of locally threshold testable (LTT) languages, our results can be useful in grammatical inference when the goal is to find a model of an LTT language from a sample of strings. We also use results of the Ehrenfeucht-Fraisse game over strings in order to handle consistent and inconsistent samples of strings. (c) 2020 Elsevier B.V. All rights reserved.
机译:我们提出了一种在从分类字符串的嘈杂样本中以析出正常形式的无量值句子提取了知识的方法。 我们展示了发现这种句子的问题是NP完成的,我们解决它的方法是基于降低到布尔满足性问题的方法。 此外,我们的方法绑定了分散的数量和每个条款的最大文字数量,因为每个条款少数人的句子和每条文字很少的文字更容易解释。 由于我们正在考虑的逻辑定义了本地阈值可测试(LTT)语言的类,我们的结果可以在语法推理中有用,当目标是从字符串样本找到LTT语言的模型时。 我们还使用Ehrenfeucht-Fraisse游戏的结果通过字符串来处理一致和不一致的字符串样本。 (c)2020 Elsevier B.v.保留所有权利。

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