Hybrid Markov logic networks
national conference on artificial intelligence(2008)
摘要
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.
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关键词
hybrid bayesian network,inference algorithm,continuous property,hybrid markov logic network,modeling language,compact modeling,markov logic network,continuous domain,markov network,first-order logic,hybrid mlns,first order logic,bayesian network,mobile robot
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