基于词典编纂的可能性逻辑知识库合并 聚合

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摘要翻译：
信念融合是人工智能中的一个重要而又困难的问题，尤其是在信息来源充满不确定性的情况下。在可能性逻辑中，许多合并算子被提出来处理这个问题，可能性逻辑是一种处理不一致性和不确定性的强大的加权逻辑。它们往往导致一个可能性知识库，它是一组加权公式。尽管可能性逻辑具有不一致性容忍性，但它却源于著名的“溺水效应”。因此，我们可能仍然希望获得一个一致的POSSI-BILISTIC知识库作为MERG-ING的结果。在这种情况下，我们认为合并后并不总是需要保持加权信息。本文定义了一个合并算子，该算子利用词典排序将一组类知识库映射为一个类知识库，并给出了一个表示完整性约束的公式。我们证明了它满足推广文[11]中命题合并基本公设的九个条件。这些公设在某种意义上抓住了最小变化的原则。然后，我们提供了一个生成合并算子的已知边基的算法。最后，我们讨论了我们的合并算子与命题合并的兼容性，以及在命题情况下我们的合并算子相对于现有语义合并算子的优势。
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英文标题：
《Merging Knowledge Bases in Possibilistic Logic by Lexicographic
  Aggregation》
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作者：
Guilin Qi, Jianfeng Du, Weiru Liu, David A. Bell
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Belief merging is an important but difficult problem in Artificial Intelligence, especially when sources of information are pervaded with uncertainty. Many merging operators have been proposed to deal with this problem in possibilistic logic, a weighted logic which is powerful for handling inconsistency and deal- ing with uncertainty. They often result in a possibilistic knowledge base which is a set of weighted formulas. Although possibilistic logic is inconsistency tolerant, it suers from the well-known "drowning effect". Therefore, we may still want to obtain a consistent possi- bilistic knowledge base as the result of merg- ing. In such a case, we argue that it is not always necessary to keep weighted informa- tion after merging. In this paper, we define a merging operator that maps a set of pos- sibilistic knowledge bases and a formula rep- resenting the integrity constraints to a clas- sical knowledge base by using lexicographic ordering. We show that it satisfies nine pos- tulates that generalize basic postulates for propositional merging given in [11]. These postulates capture the principle of minimal change in some sense. We then provide an algorithm for generating the resulting knowl- edge base of our merging operator. Finally, we discuss the compatibility of our merging operator with propositional merging and es- tablish the advantage of our merging opera- tor over existing semantic merging operators in the propositional case.
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PDF链接：
https://arxiv.org/pdf/1203.3508

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关键词：可能性 知识库 Intelligence Presentation proposition 公式 可能性 不一致性 词典 propositional