Advances in Probabilistic Logic Model and Learning
概率逻辑模型与学习研究进展

Probabilistic logic learning (PLL)research has made significant progress over the last years. A rich variety of different formalisms and learning techniques have been developed, including probabilistic relational models, bayesian logic programs, and logic bayesian networks and stochastic logic programs etc. This paper, focusing on the combination of bayesian networks and first-order logic, provides an introductory survey on probabilistic logic models based on bayesian networks through the investigation of knowledge representation, parameter estimation and structure learning algorithms. Although the PLL community has successfully demonstrated the feasibility of a number of probabilistic models for relational data, there is much work on efficiency and scalability to be done in order to begin generalizing the range and applicability of the various models.