分类知识下概率推理的魔术推理规则
Magic Inference Rules for Probabilistic Deduction under Taxonomic Knowledge
摘要：
我们提出了局部完整的推理规则的概率推理分类和概率知识库的合取事件。至关重要的是，在文献中类似的推理规则相比，我们的推理规则是局部完整的合取事件和额外的分类知识。我们发现，我们的推理规则是非常复杂的，它是乍一看不清楚，在所有推导出的最紧的界限来自哪里。此外，分析我们的推理规则的全球完整性，我们发现全球非常不完整的概率推理的例子。更一般地说，我们甚至表明，所有系统的分类和概率知识库的推理规则的合取事件是全球不完整的。我们的结论是概率演绎的迭代应用推理规则的区间限制条件概率，即使被认为是非常有前途的文献中，到目前为止，似乎非常有限的应用领域。
英文摘要：
We present locally complete inference rules for probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events. Crucially, in contrast to similar inference rules in the literature, our inference rules are locally complete for conjunctive events and under additional taxonomic knowledge. We discover that our inference rules are extremely complex and that it is at first glance not clear at all where the deduced tightest bounds come from. Moreover, analyzing the global completeness of our inference rules, we find examples of globally very incomplete probabilistic deductions. More generally, we even show that all systems of inference rules for taxonomic and probabilistic knowledge-bases over conjunctive events are globally incomplete. We conclude that probabilistic deduction by the iterative application of inference rules on interval restrictions for conditional probabilities, even though considered very promising in the literature so far, seems very limited in its field of application.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
