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
- 售價: $2,280
- 貴賓價: 9.5 折 $2,166
- 語言: 英文
- 頁數: 190
- 裝訂: Paperback
- ISBN: 1627058419
- ISBN-13: 9781627058414
-
相關分類:
人工智慧、Machine Learning 機器學習 、SQL
立即出貨 (庫存=1)
買這商品的人也買了...
-
$880$695 -
$650$514 -
$950$903 -
$1,400$1,330 -
$360$281 -
$980$833 -
$680$578 -
$490$417 -
$480$408 -
$780$616 -
$360$306 -
$620$484 -
$540$459 -
$380$323 -
$450$383 -
$594$564 -
$450$356 -
$100$95 -
$520$411 -
$450$383 -
$580$348 -
$580$493 -
$680$578 -
$480$408 -
$350$298
相關主題
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.