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SIMPSON'S PARADOX IN ARTIFICIAL INTELLIGENCE AND IN REAL LIFE

机译:辛普森悖论诊所人工智能与诊所现实生活

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“Simpson's paradox,” first described nearly a century ago, is an anomaly that sometimes arises from pooling data. Dramatic instances of the paradox have occurred in real life in the domains of epidemiology and admissions policies. Many writers have recently described hypothetical examples of the paradox arising in other areas of life and it seems possible that the paradox may occur frequently in mundane domains but with less serious implications. Thus, it is not surprising that the paradox should arise in commonsense reasoning, that subarea of artificial intelligence that seeks to axiomatize reasoning in such mundane domains. It arises as the problem “approximate proof by cases” and the question of whether to accept it may well depend on whether we wish to construct performance or competence models of reasoning. This article gives a brief history of the paradox and discusses its occurrence in our own discipline. It argues that if the paradox occurs frequently but undramatically in real life, every uncertain reasoning system will have to deal with the problem in s
机译:“辛普森悖论”在近一个世纪前首次被描述,是一种有时因汇集数据而产生的异常现象。在现实生活中,在流行病学和招生政策领域,出现了这种悖论的戏剧性例子。许多作家最近描述了在生活其他领域出现的悖论的假设例子,似乎这个悖论可能经常发生在世俗领域,但影响不那么严重。因此,悖论出现在常识推理中也就不足为奇了,常识推理是人工智能的一个子领域,它试图在这些平凡的领域中将推理公理化。它以“案例近似证明”的问题出现,是否接受它的问题很可能取决于我们是否希望构建推理的绩效或能力模型。本文简要介绍了这个悖论的历史,并讨论了它在我们自己的学科中的发生。它认为,如果悖论在现实生活中频繁发生,但并不引人注目,那么每个不确定的推理系统都必须在现实生活中处理这个问题。

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