Explainable and Interpretable Reinforcement Learning for Robotics (可解釋與可詮釋的強化學習在機器人領域的應用)

Roth, Aaron M., Manocha, Dinesh, Sriram, Ram D.

Explainable and Interpretable Reinforcement Learning for Robotics
  • 出版商: Springer
  • 出版日期: 2024-03-20
  • 售價: $2,450
  • 貴賓價: 9.5 折 $2,328
  • 語言: 英文
  • 頁數: 114
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031475178
  • ISBN-13: 9783031475177
  • 相關分類: Reinforcement、機器人製作 Robots、DeepLearning

    • 海外代購書籍(需單獨結帳)

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商品描述

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, and reactivity. The book is organized around a discussion of these methods broken down into 42 categories and subcategories, where each category can be classified according to some of the attributes. The authors close by identifying gaps in the current research and highlighting areas for future investigation.


商品描述(中文翻譯)

本書調查了可解釋和可解釋的強化學習（RL）在機器人技術中的最新進展。儘管強化學習在一般情況下已經變得越來越受歡迎，並應用於日益複雜的問題，但幾個挑戰仍然阻礙了強化學習算法在機器人技術及相關領域的實際應用。這些挑戰包括防止安全約束被違反的困難，以及系統操作員希望獲得可解釋的政策和行動所面臨的問題。機器人應用呈現出一組獨特的考量，並帶來與其物理、現實世界的感測輸入和互動相關的多個機會。

作者考慮了過去調查和論文中使用的分類技術，並試圖統一該領域的術語。本書深入探討了12個可用於分類可解釋/可解釋技術的屬性。這些屬性包括強化學習方法是否為模型無關或模型特定、自我解釋或事後解釋，以及對範圍、產生時間、格式、知識限制、解釋準確性、受眾、可預測性、可讀性、可視性和反應性等屬性的額外分析。本書圍繞這些方法的討論組織，分為42個類別和子類別，每個類別可以根據某些屬性進行分類。作者最後指出了當前研究中的空白，並強調了未來研究的領域。

作者簡介

Aaron M. Roth is currently Head of Autonomy Technology for Black Sea. Previously, Dr. Roth worked as a Research Scientist in the Distributed Autonomous Systems Group in the Navy Center for Applied Research in Artificial Intelligence at the Naval Research Laboratory and as a Researcher and Ph.D. student at the University of Maryland in the GAMMA lab. In both capacities, he led projects conducting research into autonomous robots and artificial intelligence, with specific focus on reinforcement learning, explainable/interpretable artificial intelligence, AI Safety, robot navigation, and human-robot interaction. He has also worked at several technology startups in industries spanning healthcare, finance, and mobile consumer applications. Dr. Roth's published research has been reported on by news publications worldwide, and he has presented his research speaking at international conferences in the academic research community and given talks explaining science, robotics, and artificial intelligence to the general public. He is the creator of and contributor to multiple open-source software projects. He holds a B.S.E. in Electrical Engineering from the University of Pennsylvania and an M.S. in Robotics from Carnegie Mellon University. Dr. Roth was Conference Chair for the Third Annual Artificial Intelligence Safety Unconference in 2021, an international conference about AI Safety. In both paid and volunteer capacities, Dr. Roth has provided technical mentorship to undergraduate students, graduate students, and industry career professionals across the United States, Europe, and Australia. He is also a published science fiction author and has consulted on artificial intelligence topics for creative professionals including science fiction authors and game designers.

Ram D. Sriram is currently the Chief of the Software and Systems Division, Information Technology Laboratory, at the National Institute of Standards and Technology. Before joining the Software andSystems Division. Dr. Sriram was the leader of the Design and Process group in the Manufacturing Systems Integration Division, Manufacturing Engineering Laboratory, where he conducted research on standards for interoperability of computer-aided design systems. Prior to joining NIST, he was on the engineering faculty (1986-1994) at the Massachusetts Institute of Technology (MIT) and was instrumental in setting up the Intelligent Engineering Systems Laboratory. He has extensive experience in developing knowledge-based expert systems, natural language interfaces, machine learning, object-oriented software development, life-cycle product and process models, geometrical modelers, object-oriented databases for industrial applications, health care informatics, bioinformatics, and bioimaging. Dr. Sriram has co-authored or authored nearly 300 publications, including several books. He was a founding co-editor of the International Journal for AI in Engineering. Dr. Sriram received several awards including: an NSF's Presidential Young Investigator Award (1989); ASME Design Automation Award (2011); ASME CIE Distinguished Service Award (2014); the Washington Academy of Sciences' Distinguished Career in Engineering Sciences Award (2015); ASME CIE division's Lifetime Achievement Award (2016); CMU CEE Lt. Col. Christopher Raible Distinguished Public Service Award (2018); IIT Madras Distinguished Alumnus Award (2021). He is a Fellow of AAIA, AIBME, ASME, AAAS, IEEE, IET, INCOSE, SMA, and Washington Academy of Sciences, a Distinguished Member (life) of ACM, a Senior Member (life) AAAI, and an Honorary Member of IISE. Dr. Sriram has a B.Tech. from IIT, Madras, India, and an M.S., and a Ph.D. from Carnegie Mellon University, Pittsburgh, USA.

Elham Tabassi is a Senior Research Scientist at the National Institute of Standards and Technology (NIST) and the Associate Director for Emerging Technologies in the Information Technology Laboratory (ITL). She also leads NIST's Trustworthy and Responsible AI program that aims to cultivate trust in the design, development, and use of AI technologies. As the ITL's Associate Director for Emerging Technologies, she assists NIST leadership and management at all levels in determining future strategic direction for research, development, standards, testing, and evaluation in the areas of emerging technologies such as artificial intelligence. She also coordinates interaction related to artificial intelligence with the U.S. research community, U.S. industrial community, international standards community, and other federal agencies; and provides leadership within NIST in the use of AI to solve scientific and engineering problems arising in measurement science and related use-inspired applications of AI. Dr. Tabassi has been working on various machine learning and computer vision research projects with applications in biometrics evaluation and standards since she joined NIST in 1999. She is a member of the National AI Resource Research Task Force, Vice-chair of OECD working party on AI Governance, Associate Editor of IEEE Transaction on Information Forensics and Security, and a Fellow of Washington Academy of Sciences. In 2023, TIME magazine named Elham Tabassi in its list of the 100 most influential people in AI.

Dinesh Manocha is a Distinguished University Professor at the University of Maryland. He is also the Paul Chrisman Iribe Professor of Computer Science and Electrical and Computer Engineering as well as the Phi Delta Theta/Matthew Mason Distinguished Professor Emeritus of Computer Science at Chapel Hill University of North Carolina. Dr. Mancha's research focuses on AI, robotics, computer graphics, augmented/virtual reality, and scientific computing and has published more than 750 papers. He has supervised 48 Ph.D. dissertations, and his group has won 21 best paper awards at leading conferences. His group has developed many widely used softwaresystems (with 2M+ downloads) and licensed them to more than 60 commercial vendors. He is an inventor of 17 patents, several of which have been licensed to industry. A Fellow of AAAI, AAAS, ACM, IEEE, NAI, and Sloan Foundation, Dr. Manocha is a ACM SIGGRAPH Academy Class member and Bézier Award recipient from Solid Modeling Association. He received the Distinguished Alumni Award from IIT Delhi and the Distinguished Career in Computer Science Award from Washington Academy of Sciences. He was also the co-founder of Impulsonic, a developer of physics-based audio simulation technologies, which Valve Inc acquired in November 2016. He is also a co-founder of Inception Robotics, Inc.

作者簡介(中文翻譯)

Aaron M. Roth 目前是 Black Sea 的自主技術部門負責人。之前，Roth 博士曾在海軍應用人工智慧研究中心的分散式自主系統小組擔任研究科學家，並在馬里蘭大學的 GAMMA 實驗室擔任研究員及博士生。在這兩個職位上，他主導了多個研究自主機器人和人工智慧的項目，特別專注於強化學習、可解釋/可理解的人工智慧、AI 安全、機器人導航和人機互動。他還曾在多家科技初創公司工作，涉及醫療保健、金融和移動消費應用等行業。Roth 博士的研究成果已被全球新聞媒體報導，他在國際學術會議上發表過演講，並向大眾解釋科學、機器人技術和人工智慧。他是多個開源軟體項目的創建者和貢獻者。他擁有賓夕法尼亞大學的電機工程學士學位和卡內基梅隆大學的機器人碩士學位。Roth 博士在 2021 年擔任第三屆人工智慧安全非會議的會議主席，這是一個關於 AI 安全的國際會議。在有償和志願的角色中，Roth 博士為美國、歐洲和澳洲的本科生、研究生和行業專業人士提供技術指導。他也是一位已出版的科幻小說作家，並為包括科幻作家和遊戲設計師在內的創意專業人士提供人工智慧主題的諮詢。

Ram D. Sriram 目前是國家標準與技術研究院 (NIST) 資訊技術實驗室的軟體與系統部門主管。在加入軟體與系統部門之前，Sriram 博士是製造系統整合部門設計與流程小組的負責人，進行有關電腦輔助設計系統互操作性標準的研究。在加入 NIST 之前，他曾在麻省理工學院 (MIT) 擔任工程系教職 (1986-1994)，並在設立智能工程系統實驗室方面發揮了重要作用。他在開發知識型專家系統、自然語言介面、機器學習、面向對象的軟體開發、產品和流程的生命週期模型、幾何建模、工業應用的面向對象數據庫、健康資訊學、生物資訊學和生物成像方面擁有豐富的經驗。Sriram 博士共同或獨立撰寫了近 300 篇出版物，包括幾本書籍。他是《國際工程人工智慧期刊》的創始共同編輯之一。Sriram 博士獲得了多項獎項，包括：國家科學基金會的總統青年研究者獎 (1989)；ASME 設計自動化獎 (2011)；ASME CIE 傑出服務獎 (2014)；華盛頓科學院的工程科學傑出職業獎 (2015)；ASME CIE 部門的終身成就獎 (2016)；卡內基梅隆大學土木與環境工程系的克里斯多福·雷布爾中校傑出公共服務獎 (2018)；印度馬德拉斯理工學院的傑出校友獎 (2021)。他是 AAIA、AIBME、ASME、AAAS、IEEE、IET、INCOSE、SMA 和華盛頓科學院的會士，ACM 的終身傑出會員，AAAI 的終身高級會員，以及 IISE 的名譽會員。Sriram 博士擁有印度馬德拉斯理工學院的 B.Tech. 學位，以及卡內基梅隆大學的碩士和博士學位。

Elham Tabassi 是國家標準與技術研究院 (NIST) 的高級研究科學家，並擔任資訊技術實驗室 (ITL) 新興技術的副主任。她還領導 NIST 的可信與負責任技術團隊。

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