We investigate the computational complexity of mining guarded clauses from clausal datasets through the framework of inductive logic programming (ILP). We show that learning guarded clauses is NP-complete and thus one step below the $\sigma^P_2$-complete task of learning Horn clauses on the polynomial hierarchy. Motivated by practical applications on large datasets we identify a natural tractable fragment of the problem. Finally, we also generalise all of our results to $k$-guarded clauses for constant $k$.
翻译:我们通过感性逻辑编程框架(ILP)来调查从闭锁数据集中提取的采矿保护条款的计算复杂性。 我们表明,学习保护条款是NP的完整,因此比学习关于多元等级的Horn条款的$sigma ⁇ P_2美元完成的任务要低一步。我们受大型数据集实际应用的驱使,我们发现了问题的自然可追溯的碎片。 最后,我们还将我们的所有结果概括为以美元作为保护条款的定值美元。