| 書目名稱 | Toward Robots That Reason: Logic, Probability & Causal Laws | | 編輯 | Vaishak Belle | | 視頻video | http://file.papertrans.cn/927/926807/926807.mp4 | | 概述 | Explains the need for integrating logic and probability in AI systems and the challenges that arise in doing so.Presents a model for capturing causal laws that describe dynamics and computational reas | | 叢書名稱 | Synthesis Lectures on Artificial Intelligence and Machine Learning | | 圖書封面 |  | | 描述 | 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. | | 出版日期 | Book 2023 | | 關(guān)鍵詞 | Logic Meets Probability; Probabilistic Logical Languages; Cognitive Robotics; Knowledge Representation | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-21003-7 | | isbn_softcover | 978-3-031-21005-1 | | isbn_ebook | 978-3-031-21003-7Series ISSN 1939-4608 Series E-ISSN 1939-4616 | | issn_series | 1939-4608 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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