Sum-Product-Max Networks for Tractable Decision Making: (Extended Abstract)
Sum-Product-Max Networks for Tractable Decision Making: (Extended Abstract)
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用于易处理决策的 Sum-Product-Max 网络：（扩展摘要）
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Prashant Doshi
中科院分区：
文献类型：
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作者：
Mazen Melibari;P. Poupart;Prashant Doshi
对决策的概率图模型的研究主要集中在影响图（ID）和决策电路（DC），用于表示和计算最大化预期效用的决策规则。由于ID通常是手工制作的，DC是从ID编译的，因此在本文中，我们提出了一种直接从数据中学习决策问题的结构和参数的方法。我们提出了一种新的表示称为和-产品-最大网络（SPMN），概括了和-产品网络（SPN）的类决策问题，其解决方案，类似于DC，规模线性网络的大小。我们表明，SPMN可以减少到DC线性，并提出了第一种方法从数据中学习SPMN。这种方法很重要，因为它促进了由数据驱动的易处理决策的新范式。
Investigations into probabilistic graphical models for decision making have predominantly centered on influence diagrams (IDs) and decision circuits (DCs) for representation and computation of decision rules that maximize expected utility. Since IDs are typically handcrafted and DCs are compiled from IDs, in this paper we propose an approach to learn the structure and parameters of decision-making problems directly from data. We present a new representation called sum-product-max network (SPMN) that generalizes a sum-product network (SPN) to the class of decision-making problems and whose solution, analogous to DCs, scales linearly in the size of the network. We show that SPMNs may be reduced to DCs linearly and present a first method for learning SPMNs from data. This approach is significant because it facilitates a novel paradigm of tractable decision making driven by data.