Hybrid Markov Logic Networks
Hybrid Markov Logic Networks
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发表时间：
2008-07
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通讯作者：
Jue Wang;Pedro M. Domingos
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作者：
Jue Wang;Pedro M. Domingos
马尔可夫逻辑网络（MLN）结合了联合收割机一阶逻辑和马尔可夫网络，使我们能够在一个一致的框架中处理现实世界问题的复杂性和不确定性。然而，在MLN中，所有的变量和特征都是离散的，而大多数现实世界的应用程序也包含连续的。在本文中，我们介绍了混合MLN，其中连续属性（例如，两个对象之间的距离）和它们上的函数可以表现为特征。混合MLN具有指数族中的所有分布作为特殊情况（例如，多变量高斯），并允许非独立同分布的更紧凑的建模。而不是像混合贝叶斯网络这样的命题表示。我们还介绍了混合MLN的推理算法，通过扩展的MaxWalkSAT和MC-SAT算法连续域。在移动的机器人映射域的实验-包括联合分类，聚类和回归-说明混合MLNs作为建模语言的能力，以及推理算法的准确性和效率。
Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty of real-world problems in a single consistent framework. However, in MLNs all variables and features are discrete, while most real-world applications also contain continuous ones. In this paper we introduce hybrid MLNs, in which continuous properties (e.g., the distance between two objects) and functions over them can appear as features. Hybrid MLNs have all distributions in the exponential family as special cases (e.g., multivariate Gaussians), and allow much more compact modeling of non-i.i.d. data than propositional representations like hybrid Bayesian networks. We also introduce inference algorithms for hybrid MLNs, by extending the MaxWalkSAT and MC-SAT algorithms to continuous domains. Experiments in a mobile robot mapping domain--involving joint classification, clustering and regression--illustrate the power of hybrid MLNs as a modeling language, and the accuracy and efficiency of the inference algorithms.