Toward Robots That Reason: Logic, Probability & Causal Laws (eBook)

Vaishak Belle (Autor)

eBook Download: PDF

2023 | 2023
XIII, 190 Seiten
Springer International Publishing (Verlag)
978-3-031-21003-7 (ISBN)

Infos

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.

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.

Produkt-Details

• Erscheint lt. Verlag: 20.2.2023
• Reihe/Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning
• Reihe/Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning
• Zusatzinfo: XIII, 190 p. 27 illus., 14 illus. in color.
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Technik ► Maschinenbau
• Schlagworte: agent programming • Cognitive Robotics • Knowledge Representation and Reasoning • Logic Meets Probability • Probabilistic Knowledge • Probabilistic Logical Languages • Progression Programming • Regression Programming • Situation Calculus
• ISBN-10: 3-031-21003-4 / 3031210034
• ISBN-13: 978-3-031-21003-7 / 9783031210037