Belle, Vaishak.
Toward Robots That Reason: Logic, Probability & Causal Laws [electronic resource] / by Vaishak Belle. - 1st ed. 2023. - XIII, 190 p. 27 illus., 14 illus. in color. online resource. - Synthesis Lectures on Artificial Intelligence and Machine Learning, 1939-4616 . - Synthesis Lectures on Artificial Intelligence and Machine Learning, .
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.
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. .
9783031210037
10.1007/978-3-031-21003-7 doi
Artificial intelligence.
Robotics.
Computer science--Mathematics.
Mathematical statistics.
Logic design.
Application software.
Data mining.
Artificial Intelligence.
Robotics.
Probability and Statistics in Computer Science.
Logic Design.
Computer and Information Systems Applications.
Data Mining and Knowledge Discovery.
Q334-342 TA347.A78
006.3
Toward Robots That Reason: Logic, Probability & Causal Laws [electronic resource] / by Vaishak Belle. - 1st ed. 2023. - XIII, 190 p. 27 illus., 14 illus. in color. online resource. - Synthesis Lectures on Artificial Intelligence and Machine Learning, 1939-4616 . - Synthesis Lectures on Artificial Intelligence and Machine Learning, .
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.
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. .
9783031210037
10.1007/978-3-031-21003-7 doi
Artificial intelligence.
Robotics.
Computer science--Mathematics.
Mathematical statistics.
Logic design.
Application software.
Data mining.
Artificial Intelligence.
Robotics.
Probability and Statistics in Computer Science.
Logic Design.
Computer and Information Systems Applications.
Data Mining and Knowledge Discovery.
Q334-342 TA347.A78
006.3