Toward Robots That Reason: Logic, Probability & Causal Laws

2023
ISBN: 978-3-031-21002-0
Verlag: Springer International Publishing

Buch, Englisch, 190 Seiten, Format (B × H): 173 mm x 246 mm, Gewicht: 527 g

Reihe: Synthesis Lectures on Artificial Intelligence and Machine Learning

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.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Belle, Vaishak

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik EDV | Informatik Technische Informatik Hardware: Grundlagen und Allgemeines
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Angewandte Informatik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker

Inhaltsverzeichnis

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.

Über Autor(innen)

Vaishak Belle, Ph.D., is a Chancellor’s Fellow and Reader at The University of Edinburgh School of Informatics. He is also an Alan Turing Institute Faculty Fellow, a Royal Society University Research Fellow, and a member of the Royal Society of Edinburgh’s Young Academy of Scotland. Dr. Belle directs a research lab on artificial intelligence at The University of Edinburgh, specializing in the unification of symbolic logic and machine learning. He has co-authored over 50 scientific articles on AI, and has won several best paper awards.