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A Survey of Recent Results on Spatial Reasoning via Rough Inclusions

机译:基于粗糙包含的空间推理最新结果调查

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The term rough inclusion was introduced as a generic term by Polkowski and Skowron in the seminal paper that laid foundations for Rough Mereology - a paradigm for Approximate Reasoning that combines ideas of Mereology - a set theory based on the notion of a part - with ideas of Rough Set Theory and Fuzzy Set Theory; in particular, its basic predicate of rough inclusion is a rendering of the notion of being a part to a degree. Rough Mereology is an approach towards constructing reasoning schemes that take into account uncertainty of either knowledge or concepts used in reasoning. This abstract reasoning methodology is therefore a constituent of the vast field of Cognitive Technologies (styled also Artificial Intelligence). It is well-known that mereological theories of objects have been applied in Spatial Reasoning - reasoning about uncertainty in spatial contexts. The majority of theories based on mereology and applied in reasoning about spatial objects stem from the idea of A. N. Whitehead, viz., Mereology Theory based on the predicate of being connected. In this article, we give a survey of the current state of the art in spatial reasoning based on constructs of Rough Mereology. We include here theoretical results - some of them already shown in earlier works - that witness applicability of constructs based on rough inclusions in spatial reasoning as well as we mention recent works on practical applications to real-world robot navigation.
机译:粗略包含一词是Polkowski和Skowron在开创性论文中作为通用术语引入的,它为粗糙理论学-近似推理的范式-结合了思想学的概念-基于零件概念的集合理论-与粗糙集理论和模糊集理论;特别地,它的粗略包含的基本谓词是某种程度上是一部分的概念的呈现。粗糙理论是一种构建推理方案的方法,该方案考虑了推理中所用知识或概念的不确定性。因此,这种抽象推理方法是认知技术(也称为人工智能)广阔领域的组成部分。众所周知,对象的单纯性理论已在空间推理中应用-空间上下文中的不确定性推理。多数基于纹章学并应用于空间物体推理的理论都源于A. N. Whitehead,即基于连接谓词的Mereology Theory。在本文中,我们对基于粗糙Mereology构造的空间推理技术进行了概述。我们在这里包括理论结果(其中一些已经在较早的工作中得到了证明),这些结果证明了基于空间推理中的粗略包含的构造的适用性,以及我们提到了在实际应用中应用于机器人现实导航的最新成果。

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