Toward robots that reason : logic, probability & causal laws
Author(s)
Bibliographic Information
Toward robots that reason : logic, probability & causal laws
（Synthesis lectures on artificial intelligence and machine learning）（Synthesis collection of technology）
Springer, c2023
Available at 1 libraries
Note
Bibliography: p. 181-190
Description and Table of Contents
Description
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.
Table of Contents
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.
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