Toward Robots That Reason: Logic, Probability & Causal Laws
- 3h 39m
- Vaishak Belle
- Springer
- 2023
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.
About the Author
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.
In this Book
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Introduction
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Representation Matters
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From Predicate Calculus to the Situation Calculus
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Knowledge
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Probabilistic Beliefs
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Continuous Distributions
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Localization
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Regression and Progression
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Programs
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A Modal Reconstruction
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Conclusions
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Bibliography
