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  • 1
    Online Resource
    Online Resource
    Toward robots that reason : logic, probability and causal laws (2023)
    Cham, Switzerland :Springer,
    Details
    UID:
    edoccha_9961020785002883
    Format: 1 online resource (201 pages) : , illustrations.
    ISBN: 9783031210037
    Series Statement: Synthesis lectures on artificial intelligence and machine learning
    Content: 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. .
    Note: 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.
    Additional Edition: Print version: Belle, Vaishak Toward Robots That Reason: Logic, Probability and Causal Laws Cham : Springer International Publishing AG,c2023 ISBN 9783031210020
    Language: English
    DOI: 10.1007/978-3-031-21003-7
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    Open Access Request
    Charité
  • 2
    Online Resource
    Online Resource
    Toward Robots That Reason: Logic, Probability & Causal Laws (2023)
    Cham :Springer International Publishing :
    Details
    UID:
    almahu_9949450132702882
    Format: XIII, 190 p. 27 illus., 14 illus. in color. , online resource.
    Edition: 1st ed. 2023.
    ISBN: 9783031210037
    Series Statement: Synthesis Lectures on Artificial Intelligence and Machine Learning,
    Content: 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. .
    Note: 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.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783031210020
    Additional Edition: Printed edition: ISBN 9783031210044
    Additional Edition: Printed edition: ISBN 9783031210051
    Language: English
    DOI: 10.1007/978-3-031-21003-7
    URL: https://doi.org/10.1007/978-3-031-21003-7
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    HU Berlin
  • 3
    Online Resource
    Online Resource
    Toward Robots That Reason: Logic, Probability & Causal Laws (2023)
    Cham : Springer
    Details
    UID:
    gbv_1837691339
    Format: 1 Online-Ressource (XIII, 190 Seiten) , Illustrationen
    ISBN: 9783031210037
    Series Statement: Synthesis Lectures on Artificial Intelligence and Machine Learning
    Content: 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.
    Content: 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. .
    Additional Edition: ISBN 9783031210020
    Additional Edition: ISBN 9783031210044
    Additional Edition: ISBN 9783031210051
    Additional Edition: Erscheint auch als Druck-Ausgabe Belle, Vaishak Toward robots that reason: logic, probability & causal laws Cham : Springer, 2023 ISBN 9783031210020
    Additional Edition: ISBN 9783031210051
    Language: English
    DOI: 10.1007/978-3-031-21003-7
    URL: lizenzpflichtig
    Author information: Belle, Vaishak 1983-
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    MPI Bildungsforschung
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