The problem of belief change is considered as a major issue in managing the dynamics of an information system. It consists in modifying an uncertainty distribution, representing agents’ beliefs, in the light of a new information. In this paper, we focus on the so-called multiple iterated belief revision or C-revision, proposed for conditioning or revising uncertain distributions under uncertain inputs. Uncertainty distributions are represented in terms of ordinal conditional functions. We will use prioritized or weighted knowledge bases as a compact representation of uncertainty distributions. The input information leading to a revision of an uncertainty distribution is also represented by a set of consistent weighted formulas. This paper shows that C-revision, defined at a semantic level using ordinal conditional functions, has a very natural representation using weighted knowledge bases. We propose simple syntactic methods for revising weighted knowledge bases, that are semantically meaningful in the frameworks of possibility theory and ordinal conditional functions. In particular, we show that the space complexity of the proposed syntactic C-revision is linear with respect to the size of initial weighted knowledge bases.

中文翻译：

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C修正序数条件函数的加权逻辑表示

信念变化问题被认为是管理信息系统动态的主要问题。它包括根据新信息修改不确定性分布，代表代理人的信念。在本文中，我们专注于所谓的多次迭代信念修正或 C 修正，用于在不确定输入下调节或修正不确定分布。不确定性分布用序数条件函数表示。我们将使用优先或加权的知识库作为不确定性分布的紧凑表示。导致不确定性分布修正的输入信息也由一组一致的加权公式表示。本文展示了使用序数条件函数在语义级别定义的 C 修订版，使用加权知识库具有非常自然的表示。我们提出了用于修改加权知识库的简单句法方法，这些方法在可能性理论和序数条件函数的框架中具有语义意义。特别是，我们表明，所提出的句法 C 修订版的空间复杂度与初始加权知识库的大小呈线性关系。