Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Luc De Raedt, Kristian Kersting, Sriraam Natarajan

  • 出版商: Morgan & Claypool
  • 出版日期: 2016-03-24
  • 定價: $2,280
  • 售價: 9.0$2,052
  • 語言: 英文
  • 頁數: 190
  • 裝訂: Paperback
  • ISBN: 1627058419
  • ISBN-13: 9781627058414
  • 相關分類: 人工智慧Machine LearningSQL
  • 立即出貨 (庫存=1)

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商品描述

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.

商品描述(中文翻譯)

一個與現實世界互動的智能代理將會遇到個別的人、課程、測驗結果、藥物處方、椅子、盒子等,需要推理這些個體的特性和它們之間的關係,同時應對不確定性。不確定性已在概率論和圖模型中進行研究,而關係則在邏輯中進行研究,特別是在謂詞演算和其擴展中。本書探討將邏輯和概率結合成所謂的關係概率模型的基礎。它介紹了概率、邏輯及其組合的表示、推理和學習技術。本書重點詳細介紹了兩種表示方法:馬爾可夫邏輯網絡,一種無向圖模型的關係擴展,以及加權一階謂詞演算公式,以及Problog,一種邏輯程序的概率擴展,也可以看作是貝葉斯網絡的一種圖靈完全的關係擴展。