Explainable and Interpretable Reinforcement Learning for Robotics - Rilegato

Roth, Aaron M.; Manocha, Dinesh; Sriram, Ram; Tabassi, Elham

Rilegato

ISBN 10: 3031475178 ISBN 13: 9783031475177

Casa editrice: Springer-Nature New York Inc, 2024

Riassunto

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.

Su questo libro

Informazioni sull?autore

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. 

 

Dalla quarta di copertina

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.

In addition, this book:

• 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 can enhance trust, increase adoption, reduce risk, and increase safety

Dettagli dell?edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3031475178
• ISBN 13: 9783031475177
• Rilegatura: Copertina rigida
• Numero di pagine: 132
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania