The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions

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• 关键词：Probabilistic logic programming ; Statistical relational learning ; Most Probable Explanation ; Logic programs with annotated disjunctions ; ProbLog
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9046
• 期：1
• 页码：139-153
• 全文大小：553 KB
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• 作者单位：Dimitar Shterionov (15)
  Joris Renkens (15)
  Jonas Vlasselaer (15)
  Angelika Kimmig (15)
  Wannes Meert (15)
  Gerda Janssens (15)
  
  15. KULeuven, Leuven, Belgium
  
• 丛书名：Inductive Logic Programming
• ISBN：978-3-319-23708-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Probabilistic logic languages, such as ProbLog and CP-logic, are probabilistic generalizations of logic programming that allow one to model probability distributions over complex, structured domains. Their key probabilistic constructs are probabilistic facts and annotated disjunctions to represent binary and mutli-valued random variables, respectively. ProbLog allows the use of annotated disjunctions by translating them into probabilistic facts and rules. This encoding is tailored towards the task of computing the marginal probability of a query given evidence (MARG), but is not correct for the task of finding the most probable explanation (MPE) with important applications e.g., diagnostics and scheduling.