Format:
1 Online-Ressource (XIII, 190 Seiten)
,
Illustrationen
ISBN:
9783031210037
Series Statement:
Synthesis Lectures on Artificial Intelligence and Machine Learning
Content:
Preface -- Acknowledgments -- Introduction -- Representation Matters -- From Predicate Calculus to the Situation Calculus -- Knowledge -- Probabilistic Beliefs -- Continuous Distributions -- Localization -- Regression & Progression -- Programs -- A Modal Reconstruction -- Conclusions.
Content:
This book discusses the two fundamental elements that underline the science and design of artificial intelligence (AI) systems: the learning and acquisition of knowledge from observational data, and the reasoning of that knowledge together with whatever information is available about the application at hand. It then presents a mathematical treatment of the core issues that arise when unifying first-order logic and probability, especially in the presence of dynamics, including physical actions, sensing actions and their effects. A model for expressing causal laws describing dynamics is also considered, along with computational ideas for reasoning with such laws over probabilistic logical knowledge. .
Additional Edition:
ISBN 9783031210020
Additional Edition:
ISBN 9783031210044
Additional Edition:
ISBN 9783031210051
Additional Edition:
Erscheint auch als Druck-Ausgabe Belle, Vaishak Toward robots that reason: logic, probability & causal laws Cham : Springer, 2023 ISBN 9783031210020
Additional Edition:
ISBN 9783031210051
Language:
English
DOI:
10.1007/978-3-031-21003-7
Author information:
Belle, Vaishak 1983-
