Toward Robots That Reason: Logic, Probability & Causal Laws

Vaishak Belle

ISBN 10: 3031210026 / ISBN 13: 9783031210020

Editore: Springer International Publishing, 2023

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - 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.

Codice articolo 9783031210020

Dati bibliografici

• Titolo: Toward Robots That Reason: Logic, Probability & Causal Laws
• Editore: Springer International Publishing
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 3031210026
• ISBN 13: 9783031210020
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 246 L x 173 A x 17 P (millimetri) 246 millimetri di larghezza per 173 millimetri di altezza per 17 millimetri di profondità
• Peso: 527 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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.