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Titlebook: Explainable and Interpretable Reinforcement Learning for Robotics; Aaron M. Roth,Dinesh Manocha,Elham Tabassi Book 2024 The Editor(s) (if

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發(fā)表于 2025-3-21 19:34:09 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Explainable and Interpretable Reinforcement Learning for Robotics
編輯Aaron M. Roth,Dinesh Manocha,Elham Tabassi
視頻videohttp://file.papertrans.cn/320/319305/319305.mp4
概述Provides readers with a categorization system to discuss explainable and interpretable RL techniques.Explores RL methodology specific to robotics applications.Explains how interpretable RL algorithms
叢書名稱Synthesis Lectures on Artificial Intelligence and Machine Learning
圖書封面Titlebook: Explainable and Interpretable Reinforcement Learning for Robotics;  Aaron M. Roth,Dinesh Manocha,Elham Tabassi Book 2024 The Editor(s) (if
描述.This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for robotics and related areas. These include difficulties in preventing safety constraints from being violated and the issues faced by systems operators who desire explainable policies and actions. Robotics applications present a unique set of considerations and result in? a number of opportunities related to their physical, real-world sensory input and interactions..?The authors consider classification techniques used in past surveys and papers and attempt to unify terminology across the field. The book provides an in-depth exploration of 12 attributes that can be used to classify explainable/interpretable techniques. These include whether the RL method is model-agnostic or model-specific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, a
出版日期Book 2024
關(guān)鍵詞Robot Learning; Autonomous Robotics; Safe AI; Explainable AI; Interpretable AI; Learning Systems; Intellig
版次1
doihttps://doi.org/10.1007/978-3-031-47518-4
isbn_softcover978-3-031-47520-7
isbn_ebook978-3-031-47518-4Series ISSN 1939-4608 Series E-ISSN 1939-4616
issn_series 1939-4608
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Chandra R. Bhat,Frank S. Koppelman Attributes can be robot-specific or general. General Soft Attributes include (vi) Knowledge Limits (does system understand its own applicability and limits?), (vii) Explanation Accuracy (how accurate are explanations themselves), and (viii) Audience. Robot-specific Soft Attributes include (ix) Pred
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Book 2024erpretable techniques. These include whether the RL method is model-agnostic or model-specific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, a
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1939-4608 ific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, a978-3-031-47520-7978-3-031-47518-4Series ISSN 1939-4608 Series E-ISSN 1939-4616
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Classification System, Attributes can be robot-specific or general. General Soft Attributes include (vi) Knowledge Limits (does system understand its own applicability and limits?), (vii) Explanation Accuracy (how accurate are explanations themselves), and (viii) Audience. Robot-specific Soft Attributes include (ix) Pred
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