基于语法的缺省推理作为基于概率模型的诊断
Syntax-based Default Reasoning as Probabilistic Model-based Diagnosis
摘要：
我们认为基于语法的方法默认推理作为一个基于模型的诊断问题，其中每个源给出的一条信息被认为是一个组件。它在ATMS框架中形式化（每个源对应一个假设）。我们假设所有的源都是独立的，并且以非常小的概率“失败”。这导致了对候选集的概率分配，或者等价地对一致环境集的概率分配。这种概率分配导致了一个Dempster-Shafer信念函数，它测量了一个命题可以从证据中推导出来的概率。这个信念函数可以用几种不同的方式来定义一个非单调的后果关系。我们研究和比较这些后果关系。优先知识库的情况下被简要考虑。
英文摘要：
We view the syntax-based approaches to default reasoning as a model-based diagnosis problem, where each source giving a piece of information is considered as a component. It is formalized in the ATMS framework (each source corresponds to an assumption). We assume then that all sources are independent and "fail" with a very small probability. This leads to a probability assignment on the set of candidates, or equivalently on the set of consistent environments. This probability assignment induces a Dempster-Shafer belief function which measures the probability that a proposition can be deduced from the evidence. This belief function can be used in several different ways to define a non-monotonic consequence relation. We study and compare these consequence relations. The -case of prioritized knowledge bases is briefly considered.
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