模块化信念更新与人工智能研究中可靠性度量的混淆
Modular Belief Updates and Confusion about Measures of Certainty in Artificial Intelligence Research
摘要：
在过去的十年中，人们对人工智能研究中不确定性推理的信念变化或度量的使用越来越感兴趣。几种方法的一个重要特征是信念或信念更新的变化，我们称之为模块性。我们把满足这个性质的更新称为模块化更新。尽管在19世纪首次发现了满足模块性性质的信念更新的概率度量，但在人工智能研究中，对这些量的知识和讨论仍然模糊不清。我们定义了模块化更新，并讨论了它们在两个有影响力的专家系统中的不当使用。
英文摘要：
Over the last decade, there has been growing interest in the use or measures or change in belief for reasoning with uncertainty in artificial intelligence research. An important characteristic of several methodologies that reason with changes in belief or belief updates, is a property that we term modularity. We call updates that satisfy this property modular updates. Whereas probabilistic measures of belief update - which satisfy the modularity property were first discovered in the nineteenth century, knowledge and discussion of these quantities remains obscure in artificial intelligence research. We define modular updates and discuss their inappropriate use in two influential expert systems.
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。
