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On the role of modeling causal independence for system model compilation with OBDDs

机译:关于因果关系建模对使用OBDD进行系统模型编译的作用

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Model-Based Reasoning requires as input a formal model of the system often expressed as a prepositional logic theory. Exploiting the presence of structure in such a theory is fundamental in order to have a compact representation of the model and, more important, to speed-up the reasoning task. In this paper we introduce the notion of causal independence (derived from the Bayesian Networks formalism) in order to allow the modeling of an important class of local relations among system variables. In particular we focus our analysis on MAX families, where the value of a common effect is determined as the maximum among the independent contributions of a set of causing variables. We show formal and experimental results on the positive effects of causal independence on the size of the compilation of the system model in terms of an Ordered Binary Decision Diagram and connect them with the computational efficiency of Model-Based Diagnosis. Such benefits hold also when we relax the notion of causal independence in order to cover a broader class of systems which includes combinatorial digital circuits.
机译:基于模型的推理需要通常以介词逻辑理论表达的系统形式模型作为输入。在这种理论中利用结构的存在是基本的,以便对模型有一个紧凑的表示,更重要的是,它可以加快推理任务。在本文中,我们介绍了因果独立性的概念(源自贝叶斯网络形式主义),以便对系统变量之间的重要局部关系建模。特别是,我们将分析重点放在MAX系列上,在该系列中,一个共同效应的值被确定为一组引起变量的独立贡献中的最大值。我们用有序二元决策图显示了因果独立性对系统模型编译大小的积极影响的正式和实验结果,并将它们与基于模型的诊断的计算效率联系起来。当我们放松因果独立性的概念以涵盖包括组合数字电路的更广泛的系统类别时,这些好处也将保留。

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