Some Issues on Nondeterministic Knowledge Bases with Incomplete and Selective Information
Some Issues on Nondeterministic Knowledge Bases with Incomplete and Selective Information
复制标题
不完全和选择性信息的非确定性知识库的一些问题
DOI：
10.1007/3-540-69115-4_58
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
H. Sakai
中科院分区：
文献类型：
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作者：
H. Sakai
粗糙集理论依赖于确定性信息系统或知识库，正成为软计算的数学基础。在本文中，我们拿起不确定的知识库与不完全和选择性的信息。这两种信息都是以属性值的形式给出的，它们的区别来自于时态概念。如果信息是指过去的信息，那么我们认为它是不完整的信息。另一方面，选择性信息意味着真实的属性值不是在集合中决定的，即，我们可以从这个集合中选择最合适的值。通过将这两种信息引入到知识库中，我们开发了另一种不确定性知识库的框架。即，我们讨论的问题回答，近似，粗糙集的概念和属性的依赖关系的不确定性知识库。
Rough set theory depending upon deterministic information systems or knowledge bases is now becoming a mathematical foundation of soft computing. In this paper, we pick up nondeterministic knowledge bases with incomplete and selective information. The both information are given as a set of attribute values, whose difference comes from the temporal concept. If the information is referring the past information then we see it incomplete information. On the other hand, selective information means that the real attribute value is not decided in a set, i.e., we can select the most proper value from this set. By introducing these two information into knowledge bases, we develop another framework for nondeterministic knowledge bases. Namely, we discuss question-answering, approximation, rough set concept and dependencies of attributes on this nondeterministic knowledge bases.