| 书目名称 | Statistical Relational Artificial Intelligence | | 副标题 | Logic, Probability, | | 编辑 | Luc Raedt,Kristian Kersting,David Poole | | 视频video | | | 丛书名称 | Synthesis Lectures on Artificial Intelligence and Machine Learning | | 图书封面 |  | | 描述 | An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks. | | 出版日期 | Book 2016 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01574-8 | | isbn_ebook | 978-3-031-01574-8Series ISSN 1939-4608 Series E-ISSN 1939-4616 | | issn_series | 1939-4608 | | copyright | Springer Nature Switzerland AG 2016 |
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发表于 2025-3-21 17:18:02
