Toward Robots That Reason: Logic, Probability & Causal Laws

Vaishak Belle

ISBN 10: 3031210050 / ISBN 13: 9783031210051

Editore: Springer International Publishing, Springer Nature Switzerland Feb 2024, 2024

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Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 204 pp. Englisch.

Codice articolo 9783031210051

Dati bibliografici

• Titolo: Toward Robots That Reason: Logic, Probability & Causal Laws
• Editore: Springer International Publishing, Springer Nature Switzerland Feb 2024
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3031210050
• ISBN 13: 9783031210051
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 240 L x 168 A x 12 P (millimetri) 240 millimetri di larghezza per 168 millimetri di altezza per 12 millimetri di profondità
• Peso: 352 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.