将知识分布到简单的基础中
Distributing Knowledge into Simple Bases
摘要：
在过去的几年里，理解经典逻辑片段的信念改变算子的行为受到了越来越多的关注。在这个方向上的结果主要涉及适应表示定理。然而，碎片驱动的信念变化也导致了新的研究问题。在本文中，我们提出了信念分布的概念，这可以理解为合并的反向任务。更具体地说，我们感兴趣的是以下问题：给定一个任意的知识库$K$和一些合并操作$Δ$，我们可以找到一个配置文件$E$和一个约束$μ$，都从一个给定的片段的经典逻辑，使$Δ_μ(E)$产生一个结果相当于$K$？换句话说，我们感兴趣的是，如果$K$可以被分布到更简单的结构的知识库，这样的合并任务允许重建的原始知识。我们的初步结果表明，合并的基础上激烈的距离允许一个简单的知识分布，而权力的分布运营商的基础上汉明距离在很大程度上依赖于选择的片段。
英文摘要：
Understanding the behavior of belief change operators for fragments of classical logic has received increasing interest over the last years. Results in this direction are mainly concerned with adapting representation theorems. However, fragment-driven belief change also leads to novel research questions. In this paper we propose the concept of belief distribution, which can be understood as the reverse task of merging. More specifically, we are interested in the following question: given an arbitrary knowledge base $K$ and some merging operator $Δ$, can we find a profile $E$ and a constraint $μ$, both from a given fragment of classical logic, such that $Δ_μ(E)$ yields a result equivalent to $K$? In other words, we are interested in seeing if $K$ can be distributed into knowledge bases of simpler structure, such that the task of merging allows for a reconstruction of the original knowledge. Our initial results show that merging based on drastic distance allows for an easy distribution of knowledge, while the power of distribution for operators based on Hamming distance relies heavily on the fragment of choice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
