Probabilistic Inference Modulo Theories
Probabilistic Inference Modulo Theories
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概率推理模理论
DOI：
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发表时间：
2016
期刊：
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通讯作者：
R. Dechter
中科院分区：
文献类型：
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作者：
Rodrigo de Salvo Braz;Ciaran O'Reilly;Vibhav Gogate;R. Dechter
我们提出了SGDPLL（T），一个算法，解决了（在许多其他问题中）概率推理模理论，即通过提供作为参数的逻辑理论定义的概率模型上的推理问题（目前，命题，离散排序上的等式，以及不等式，更具体地说，有界整数上的差分运算）。虽然已经提出了对逻辑表示的概率推理的许多解决方案，但SGDPLL（T）同时（1）提升，（2）精确和（3）模理论，即，由背景逻辑理论参数化。这为将其扩展到丰富的逻辑语言（如数据结构和关系数据）提供了基础。所谓提升，我们指的是在域大小（变量可以取值的数量）上具有恒定复杂度的算法。我们还详细介绍了一个求解器的求和差分算法，并显示实验结果的情况下，SGDPLL（T）是比一个国家的最先进的概率求解器快得多。
We present SGDPLL(T), an algorithm that solves (among many other problems) probabilistic inference modulo theories, that is, inference problems over probabilistic models defined via a logic theory provided as a parameter (currently, propositional, equalities on discrete sorts, and inequalities, more specifically difference arithmetic, on bounded integers). While many solutions to probabilistic inference over logic representations have been proposed, SGDPLL(T) is simultaneously (1) lifted, (2) exact and (3) modulo theories, that is, parameterized by a background logic theory. This offers a foundation for extending it to rich logic languages such as data structures and relational data. By lifted, we mean algorithms with constant complexity in the domain size (the number of values that variables can take). We also detail a solver for summations with difference arithmetic and show experimental results from a scenario in which SGDPLL(T) is much faster than a state-of-the-art probabilistic solver.