Abstract

The problems of artificial intelligence, control and decision-making with incomplete or unreliable information include a wide class of problems of abductive explanation of the observed, including cause–effect problems. The study is devoted to the substantiation of the method of logical formation of hypotheses explaining the observed. Means of knowledge representation and derivation of hypotheses are proposed. A language possessing the property of substitutability is introduced. The properties of language and the calculi introduced in it provide hypothesizing by combining deduction and abduction. In contrast to the well-known logical methods of abduction, the proposed techniques make it possible to derive hypotheses (minorants) that are necessary and sufficient for a formal explanation of the observed. Based on minorants in combination with the basic theory of the subject area, reliable causes of the observed or relevant circumstances leading to these causes are formed. In this case, in situations with the availability of empirical data, these causes and circumstances can also be formed in plausible versions. Examples from technology and medicine are considered.

中文翻译：

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解释被观察者问题中的归纳推理方法

摘要

信息不完整或不可靠的人工智能，控制和决策问题包括对观察到的事物进行归纳解释的各种问题，包括因果关系问题。该研究致力于证实解释所观察到的假设的逻辑形式的方法的证实。提出了知识表示和假设推导的方法。介绍了一种具有可替代性的语言。语言的性质和其中引入的结石通过演绎和绑架相结合提供了假设。与众所周知的绑架逻辑方法相比，所提出的技术使得有可能推导出对所观察到的形式进行解释所必需和充分的假设（伪造）。基于未成年人与主题领域的基本理论相结合，形成了导致这些原因的观察到的可靠原因或相关情况。在这种情况下，在有经验数据可用的情况下，这些原因和情况也可以合理的形式形成。考虑了来自技术和医学的例子。