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Reasoning over Linear Probabilistic Knowledge Bases with Priorities

机译:优先考虑线性概率知识库的推理

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We consider the problem of reasoning over probabilistic knowledge bases with different priority levels. While we assume that the knowledge is consistent on each level, there can be inconsistencies between different levels. Examples arise naturally in hierarchical domains when general knowledge is overwritten with more specific information. We extend recent results on inconsistency-tolerant probabilistic reasoning to propose a solution for this problem.
机译:我们考虑对优先级不同的概率知识库进行推理的问题。尽管我们假设知识在每个级别上都是一致的,但不同级别之间可能存在不一致之处。当普通知识被更具体的信息覆盖时,示例自然会出现在层次结构域中。我们扩展了关于不一致容忍概率推理的最新结果,以提出针对此问题的解决方案。

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