Reinforcement Learning for Cyber Physical Systems

This book aims to establish a linkage between the two domains by systematically introducing RL foundations and algorithms, each supported by one or a few state-of-the-art CPS examples to help readers understand the intuition and usefulness ...

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Author: Chong Li

Publisher: CRC Press

ISBN: 9781351006606

Category: Computers

Page: 238

View: 325

Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by recent developments in the fields of reinforcement learning (RL) and cyber-physical systems (CPSs). Rooted in behavioral psychology, RL is one of the primary strands of machine learning. Different from other machine learning algorithms, such as supervised learning and unsupervised learning, the key feature of RL is its unique learning paradigm, i.e., trial-and-error. Combined with the deep neural networks, deep RL become so powerful that many complicated systems can be automatically managed by AI agents at a superhuman level. On the other hand, CPSs are envisioned to revolutionize our society in the near future. Such examples include the emerging smart buildings, intelligent transportation, and electric grids. However, the conventional hand-programming controller in CPSs could neither handle the increasing complexity of the system, nor automatically adapt itself to new situations that it has never encountered before. The problem of how to apply the existing deep RL algorithms, or develop new RL algorithms to enable the real-time adaptive CPSs, remains open. This book aims to establish a linkage between the two domains by systematically introducing RL foundations and algorithms, each supported by one or a few state-of-the-art CPS examples to help readers understand the intuition and usefulness of RL techniques. Features Introduces reinforcement learning, including advanced topics in RL Applies reinforcement learning to cyber-physical systems and cybersecurity Contains state-of-the-art examples and exercises in each chapter Provides two cybersecurity case studies Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies is an ideal text for graduate students or junior/senior undergraduates in the fields of science, engineering, computer science, or applied mathematics. It would also prove useful to researchers and engineers interested in cybersecurity, RL, and CPS. The only background knowledge required to appreciate the book is a basic knowledge of calculus and probability theory.

Reinforcement Learning for Cyber Physical Systems

Other toolkits or testbeds for (deep) reinforcement learning with more
complicated environments includes OpenAI Universe ... Overview of Cyber-
Physical Systems and Cybersecurity CONTENTS 2.1 2.2 2.3 20 □
Reinforcement Learning for ...

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Author: Chong Li

Publisher: CRC Press

ISBN: 9781351006613

Category: Computers

Page: 238

View: 231

Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by recent developments in the fields of reinforcement learning (RL) and cyber-physical systems (CPSs). Rooted in behavioral psychology, RL is one of the primary strands of machine learning. Different from other machine learning algorithms, such as supervised learning and unsupervised learning, the key feature of RL is its unique learning paradigm, i.e., trial-and-error. Combined with the deep neural networks, deep RL become so powerful that many complicated systems can be automatically managed by AI agents at a superhuman level. On the other hand, CPSs are envisioned to revolutionize our society in the near future. Such examples include the emerging smart buildings, intelligent transportation, and electric grids. However, the conventional hand-programming controller in CPSs could neither handle the increasing complexity of the system, nor automatically adapt itself to new situations that it has never encountered before. The problem of how to apply the existing deep RL algorithms, or develop new RL algorithms to enable the real-time adaptive CPSs, remains open. This book aims to establish a linkage between the two domains by systematically introducing RL foundations and algorithms, each supported by one or a few state-of-the-art CPS examples to help readers understand the intuition and usefulness of RL techniques. Features Introduces reinforcement learning, including advanced topics in RL Applies reinforcement learning to cyber-physical systems and cybersecurity Contains state-of-the-art examples and exercises in each chapter Provides two cybersecurity case studies Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies is an ideal text for graduate students or junior/senior undergraduates in the fields of science, engineering, computer science, or applied mathematics. It would also prove useful to researchers and engineers interested in cybersecurity, RL, and CPS. The only background knowledge required to appreciate the book is a basic knowledge of calculus and probability theory.

Big Data Analytics for Cyber Physical Systems

This book focuses on the interaction between IoT technology and the mathematical tools used to evaluate the extracted data of those systems.

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Author: Guido Dartmann

Publisher: Elsevier

ISBN: 9780128166468

Category: Law

Page: 396

View: 538

Big Data Analytics in Cyber-Physical Systems: Machine Learning for the Internet of Things examines sensor signal processing, IoT gateways, optimization and decision-making, intelligent mobility, and implementation of machine learning algorithms in embedded systems. This book focuses on the interaction between IoT technology and the mathematical tools used to evaluate the extracted data of those systems. Each chapter provides the reader with a broad list of data analytics and machine learning methods for multiple IoT applications. Additionally, this volume addresses the educational transfer needed to incorporate these technologies into our society by examining new platforms for IoT in schools, new courses and concepts for universities and adult education on IoT and data science. . Bridges the gap between IoT, CPS, and mathematical modelling. Features numerous use cases that discuss how concepts are applied in different domains and applications. Provides "best practices", "winning stories" and "real-world examples" to complement innovation. Includes highlights of mathematical foundations of signal processing and machine learning in CPS and IoT.

Machine Learning for Cyber Physical Systems

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions.

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Author: Jürgen Beyerer

Publisher: Springer

ISBN: 9783662590843

Category: Technology & Engineering

Page: 87

View: 688

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 25th-26th, 2017. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

Machine Learning for Cyber Physical Systems

This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions.

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Author: Jürgen Beyerer

Publisher: Springer

ISBN: 9783662584859

Category: Technology & Engineering

Page: 136

View: 437

This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

Machine Learning for Cyber Physical Systems

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions.

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Author: Jürgen Beyerer

Publisher: Springer

ISBN: 9783662538067

Category: Technology & Engineering

Page: 72

View: 492

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, September 29th, 2016. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

Machine Learning for Cyber Physical Systems

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions.

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Author: Jürgen Beyerer

Publisher: Springer Vieweg

ISBN: 3662538059

Category: Computers

Page: 69

View: 738

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, September 29th, 2016. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

Resilient and Safe Control of Cyber physical Systems Under Uncertainties and Adversaries

The recent growth of cyber-physical systems with a wide range of applications such as smart grids, healthcare, search and rescue and traffic monitoring, to name a few, brings new challenges to control systems due to the presence of ...

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Author: Aquib Mustafa

Publisher:

ISBN: 9798664738759

Category: Electronic dissertations

Page: 187

View: 824

The recent growth of cyber-physical systems with a wide range of applications such as smart grids, healthcare, search and rescue and traffic monitoring, to name a few, brings new challenges to control systems due to the presence of significant uncertainties and undesired signals (i.e., disturbances and cyber-physical attacks). Thus, it is of vital importance to design resilient and safe control approaches that can adapt to the situation and mitigate adversaries to ensure an acceptable level of functionality and autonomy despite uncertainties and cyber-physical attacks.This dissertation begins with the analysis of adversaries and design of resilient distributed control mechanisms for multi-agent cyber-physical systems with guaranteed performance and consensus under mild assumptions. More specifically, the adverse effects of cyber-physical attacks are first analyzed on the synchronization of the multi-agent cyber-physical systems. Then, information-theoretic based detection and mitigation methods are presented by equipping agents with self-belief about the trustworthiness of their own information and trust about their neighbors. Then, the effectiveness of the developed approach is certified by applying it to distributed frequency and voltage synchronization of AC microgrids under data manipulation attacks. In the next step, to relax some connectivity assumptions in the network for the resilient control design, a distributed adaptive attack compensator is developed by estimating the normal expected behavior of agents. The adaptive attack compensator is augmented with the controller and it is shown that the proposed controller achieves resilient synchronization in the presence of the attacks on sensors and actuators. Moreover, this approach recovers compromised agents under actuator attacks and avoids propagation of attacks on sensors without discarding information from the compromised agents. Then, the problem of secure state estimation for distributed sensor networks is considered. More specifically, the adverse effects of cyber-physical attacks on distributed sensor networks are analyzed and attack mitigation mechanism for the event-triggered distributed Kalman filter is presented. It is shown that although event-triggered mechanisms are highly desirable, the attacker can leverage the event-triggered mechanism to cause triggering misbehaviors which significantly harms the network connectivity and performance. Then, an entropy estimation-based attack detection and mitigation mechanisms are designed.Finally, the safe reinforcement learning framework for autonomous control systems under constraints is developed. Reinforcement learning agents with pre-specified reward functions cannot provide guaranteed safety across variety of circumstances that an uncertain system might encounter. To guarantee performance while assuring the satisfaction of safety constraints across variety of circumstances, an assured autonomous control framework is designed by empowering reinforcement learning algorithms with meta-cognitive learning capabilities. More specifically, adapting the reward function parameters of the reinforcement learning agent is performed in a meta-cognitive decision-making layer to assure the feasibility of the reinforcement learning agent.

Cyber Physical Systems Security

The chapters in this book present the work of researchers, scientists, engineers, and teachers engaged with developing unified foundations, principles, and technologies for cyber-physical security.

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Author: Çetin Kaya Koç

Publisher: Springer

ISBN: 9783319989358

Category: Computers

Page: 344

View: 181

The chapters in this book present the work of researchers, scientists, engineers, and teachers engaged with developing unified foundations, principles, and technologies for cyber-physical security. They adopt a multidisciplinary approach to solving related problems in next-generation systems, representing views from academia, government bodies, and industrial partners, and their contributions discuss current work on modeling, analyzing, and understanding cyber-physical systems.

Frontiers Of Intelligent Control And Information Processing

This requires the support of cognitive components, and communication protocol to synchronize events within the system to operate in unison.In this review volume, we invited several well-known experts and active researchers from ...

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Author: Derong Liu

Publisher: World Scientific

ISBN: 9789814616898

Category: Technology & Engineering

Page: 480

View: 828

The current research and development in intelligent control and information processing have been driven increasingly by advancements made from fields outside the traditional control areas, into new frontiers of intelligent control and information processing so as to deal with ever more complex systems with ever growing size of data and complexity.As researches in intelligent control and information processing are taking on ever more complex problems, the control system as a nuclear to coordinate the activity within a system increasingly need to be equipped with the capability to analyze, and reason so as to make decision. This requires the support of cognitive components, and communication protocol to synchronize events within the system to operate in unison.In this review volume, we invited several well-known experts and active researchers from adaptive/approximate dynamic programming, reinforcement learning, machine learning, neural optimal control, networked systems, and cyber-physical systems, online concept drift detection, pattern recognition, to contribute their most recent achievements into the development of intelligent control systems, to share with the readers, how these inclusions helps to enhance the cognitive capability of future control systems in handling complex problems.This review volume encapsulates the state-of-art pioneering works in the development of intelligent control systems. Proposition and evocations of each solution is backed up with evidences from applications, could be used as references for the consideration of decision support and communication components required for today intelligent control systems.

Data efficient Analytics for Optimal Human Cyber Physical Systems

The goal of this research is to enable optimal human-cyber-physical systems (h-CPS) by data-efficient analytics.

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Author: Ming Jin

Publisher:

ISBN: OCLC:1031367881

Category:

Page: 169

View: 523

The goal of this research is to enable optimal human-cyber-physical systems (h-CPS) by data-efficient analytics. The capacities of societal-scale infrastructures such as smart buildings and power grids are rapidly increasing, becoming physical systems capable of cyber computation that can deliver human-centric services while enhancing efficiency and resilience. Because people are central to h-CPS, the first part of this thesis is dedicated to learning about the human factors, including both human behaviors and preferences. To address the central challenge of data scarcity, we propose physics-inspired sensing by proxy and a framework of "weak supervision" to leverage high-level heuristics from domain knowledge. To infer human preferences, our key insight is to learn a functional abstraction that can rationalize people's behaviors. Drawing on this insight, we develop an inverse game theory framework that determines people's utility functions by observing how they interact with one another in a social game to conserve energy. We further propose deep Bayesian inverse reinforcement learning, which simultaneously learns a motivator representation to expand the capacity of modeling complex rewards and rationalizes an agent's sequence of actions to infer its long-term goals. Enabled by this contextual awareness of the human, cyber, and physical states, we introduce methods to analyze and enhance system-level efficiency and resilience. We propose an energy retail model that enables distributed energy resource utilization and that exploits demand-side flexibility. The synergy that naturally emerges from integrated optimization of thermal and electrical energy provision substantially improves efficiency and economy. While data empowers the aforementioned h-CPS learning and control, malicious attacks can pose major security threats. The cyber resilience of power system state estimation is analyzed. The envisioning process naturally leads to a power grid resilience metric to guide "grid hardening." While the methods introduced in the thesis can be applied to many h-CPS systems, this thesis focuses primarily on the implications for smart buildings and smart grid.

Game Theory and Machine Learning for Cyber Security

This book describes a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges.

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Author: Charles A. Kamhoua

Publisher: Wiley-IEEE Press

ISBN: 1119723922

Category:

Page: 500

View: 901

This book describes a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. It begins by introducing basic concepts on game theory, machine learning, cyber security and cyber deception. Further chapters bring together the best researchers and practitioners in cyber security to share their latest research contributions in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. The book provides expert insights on applying these new methods to address cyber autonomy, 5G security, blockchain technology, attack graphs, sensor manipulation, fault injection, moving target defense, Cyber-Physical Systems (CPS), Internet-of-Battle- Things (IoBT), multi-domain battle. The book closes by summarizing ongoing research topics in cyber security and points to open issues and future research challenges.

Proactive and Dynamic Network Defense

This book discusses and summarizes current research issues, identifies challenges, and outlines future directions for proactive and dynamic network defense.

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Author: Cliff Wang

Publisher: Springer

ISBN: 9783030105976

Category: Computers

Page: 264

View: 612

This book discusses and summarizes current research issues, identifies challenges, and outlines future directions for proactive and dynamic network defense. This book also presents the latest fundamental research results toward understanding proactive and dynamic network defense by top researchers in related areas. It includes research results that offer formal frameworks to define proactive and dynamic network defense, and develop novel models to analyze and evaluate proactive designs and strategies in computer systems, network systems, cyber-physical systems and wireless networks. A wide variety of scientific techniques have been highlighted to study these problems in the fundamental domain. As the convergence of our physical and digital worlds grows fast pace, protecting information systems from being tampered or unauthorized access is becoming one of the most importance issues. The traditional mechanisms of network defense are built upon a static, passive, and reactive nature, which has insufficient to defend against today's attackers that attempt to persistently analyze, probe, circumvent or fool such mechanisms. It has not yet been fully investigated to address the early stage of “cyber kill chain” when adversaries carry out sophisticated reconnaissance to plan attacks against a defense system. Recently, proactive and dynamic network defense has been proposed as an important alternative towards comprehensive network defense. Two representative types of such defense are moving target defense (MTD) and deception-based techniques. These emerging approaches show great promise to proactively disrupt the cyber-attack kill chain and are increasingly gaining interest within both academia and industry. However, these approaches are still in their preliminary design stage. Despite the promising potential, there are research issues yet to be solved regarding the effectiveness, efficiency, costs and usability of such approaches. In addition, it is also necessary to identify future research directions and challenges, which is an essential step towards fully embracing proactive and dynamic network defense. This book will serve as a great introduction for advanced-level computer science and engineering students who would like to start R&D efforts in the field of proactive and dynamic network defense. Researchers and professionals who work in this related field will also find this book useful as a reference.

Adaptive Autonomous Secure Cyber Systems

This book targets cyber-security professionals and researchers (industry, governments, and military). Advanced-level students in computer science and information systems will also find this book useful as a secondary textbook.

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Author: Sushil Jajodia

Publisher: Springer Nature

ISBN: 9783030334321

Category: Computers

Page: 289

View: 689

This book explores fundamental scientific problems essential for autonomous cyber defense. Specific areas include: Game and control theory-based moving target defenses (MTDs) and adaptive cyber defenses (ACDs) for fully autonomous cyber operations; The extent to which autonomous cyber systems can be designed and operated in a framework that is significantly different from the human-based systems we now operate; On-line learning algorithms, including deep recurrent networks and reinforcement learning, for the kinds of situation awareness and decisions that autonomous cyber systems will require; Human understanding and control of highly distributed autonomous cyber defenses; Quantitative performance metrics for the above so that autonomous cyber defensive agents can reason about the situation and appropriate responses as well as allowing humans to assess and improve the autonomous system. This book establishes scientific foundations for adaptive autonomous cyber systems and ultimately brings about a more secure and reliable Internet. The recent advances in adaptive cyber defense (ACD) have developed a range of new ACD techniques and methodologies for reasoning in an adaptive environment. Autonomy in physical and cyber systems promises to revolutionize cyber operations. The ability of autonomous systems to execute at scales, scopes, and tempos exceeding those of humans and human-controlled systems will introduce entirely new types of cyber defense strategies and tactics, especially in highly contested physical and cyber environments. The development and automation of cyber strategies that are responsive to autonomous adversaries pose basic new technical challenges for cyber-security. This book targets cyber-security professionals and researchers (industry, governments, and military). Advanced-level students in computer science and information systems will also find this book useful as a secondary textbook.

Computer Safety Reliability and Security

This book constitutes the refereed proceedings of five workshops co-located with SAFECOMP 2018, the 37th International Conference on Computer Safety, Reliability, and Security, held in Västerås, Sweden, in September 2018.

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Author: Barbara Gallina

Publisher: Springer

ISBN: 9783319992297

Category: Computers

Page: 564

View: 918

This book constitutes the refereed proceedings of five workshops co-located with SAFECOMP 2018, the 37th International Conference on Computer Safety, Reliability, and Security, held in Västerås, Sweden, in September 2018. The 28 revised full papers and 21 short papers presented together with 5 introductory papers to each workshop were carefully reviewed and selected from 73 submissions. This year's workshops are: ASSURE 2018 – Assurance Cases for Software-Intensive Systems; DECSoS 2018 – ERCIM/EWICS/ARTEMIS Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems; SASSUR 2018 – Next Generation of System Assurance Approaches for Safety-Critical Systems; STRIVE 2018 – Safety, securiTy, and pRivacy In automotiVe systEms; and WAISE 2018 – Artificial Intelligence Safety Engineering.

Control of Complex Systems

This book is intended for researchers and control engineers in machine learning, adaptive control, optimization and automatic control systems, including Electrical Engineers, Computer Science Engineers, Mechanical Engineers, ...

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Author: Kyriakos Vamvoudakis

Publisher: Butterworth-Heinemann

ISBN: 9780128054376

Category: Technology & Engineering

Page: 762

View: 524

In the era of cyber-physical systems, the area of control of complex systems has grown to be one of the hardest in terms of algorithmic design techniques and analytical tools. The 23 chapters, written by international specialists in the field, cover a variety of interests within the broader field of learning, adaptation, optimization and networked control. The editors have grouped these into the following 5 sections: “Introduction and Background on Control Theory”, “Adaptive Control and Neuroscience”, “Adaptive Learning Algorithms”, “Cyber-Physical Systems and Cooperative Control”, “Applications”. The diversity of the research presented gives the reader a unique opportunity to explore a comprehensive overview of a field of great interest to control and system theorists. This book is intended for researchers and control engineers in machine learning, adaptive control, optimization and automatic control systems, including Electrical Engineers, Computer Science Engineers, Mechanical Engineers, Aerospace/Automotive Engineers, and Industrial Engineers. It could be used as a text or reference for advanced courses in complex control systems. • Collection of chapters from several well-known professors and researchers that will showcase their recent work • Presents different state-of-the-art control approaches and theory for complex systems • Gives algorithms that take into consideration the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals and malicious attacks compromising the security of networked teams • Real system examples and figures throughout, make ideas concrete Includes chapters from several well-known professors and researchers that showcases their recent work Presents different state-of-the-art control approaches and theory for complex systems Explores the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals, and malicious attacks compromising the security of networked teams Serves as a helpful reference for researchers and control engineers working with machine learning, adaptive control, and automatic control systems

Moral Difference Between Scientist

The best or most reasonable achievement aims to avoid any traffic accident occurrences on roads.

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Author: Johnny Ch Lok

Publisher: Independently Published

ISBN: 1070221775

Category:

Page: 150

View: 711

The best or most reasonable achievement aims to avoid any traffic accident occurrences on roads. Thus, (AI) non-manual driving motor manufacturers have ethic or moral responsibilities to invent the most safe driving machine learning systems modelling and programming, such as deep driving learning systems and programming, reinforcement learning and (AI)-based systems is particularly challenging in safety-critical applications, such as autonomous vehicles, personal care or assistive robots and collaborative industrial robots.(AI) robot scientists need to intend to explore new ideas on safety engineering for (AI) based systems, ethically design, regulation and standards for (AI) based systems. In particular, they need to spend time to attend any meetings and in depth discussions about different safe (AI) robots issues, bounded morality, safety, safe human-machine interaction and safety considerations in automated decision making systems in a way that makes (AI)-based systems can achieve more trustworthy, accountable and ethically useful functions to any (AI) robot users. Thus, (AI) scientists need to concern diverse communities, such as (AI) safety engineering, ethics, standardization and robotic cyber-physical systems, safety critical systems, and application domain communities, such as (AI) non-human driving automate, (AI) healthcare robots, (AI) manufacturing robots, (AI) agriculture robots, (AI) aerospace robots, critical infrastructures, and (AI) retail robots safe use issues for (AI) users.However, (AI) safe discussion topics ought concern such as: How to avoid (AI) negative useful effects, safety in (AI) based system design, runtime monitoring and self-adaptation of (AI) safety, safe machine learning, safety constraints and rules in decision making systems, continuous and validation of safety properties, (AI) based system predictability, model-based engineering approaches to (AI) safety, ethically design of (AI) based system, machine-readable representations of ethical principles and rules, (AI) values and goals problem, accountability, responsibility and liability of (AI) based systems, uncertainty in (AI), all safety risk assessment and reduction, loss of values and confidence, self-esteem and the distributional shift problem, reward hacking and training corruption, weapon of (AI) based systems invention avoidance of explanation, self criticism problem, simulation for safe exploration and training problem, human machine interaction safety problem, (AI) applied to safety engineering problem, regulating (AI) based systems, safety standards, (AI) discrimination, human-in-the-hoop and the scalable oversight problem, experiences in (AI) based safety-critical systems include industrial processes, health automate system. All above safe issues will be (AI) scientists who need to discuss how to solve (AI) safe challenges.

Organic Computing

Key Terms: Autonomic Computing, ProActive Computing, ComplexAdaptive System, Self-Organisation

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Author: Tomforde, Sven

Publisher: kassel university press GmbH

ISBN: 9783737603003

Category:

Page: 134

View: 816

Key Terms: Autonomic Computing, ProActive Computing, ComplexAdaptive System, Self-Organisation

Safe Autonomous and Intelligent Vehicles

This book covers the start-of-the-art research and development for the emerging area of autonomous and intelligent systems.

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Author: Huafeng Yu

Publisher: Springer

ISBN: 9783319973012

Category: Technology & Engineering

Page: 204

View: 431

This book covers the start-of-the-art research and development for the emerging area of autonomous and intelligent systems. In particular, the authors emphasize design and validation methodologies to address the grand challenges related to safety. This book offers a holistic view of a broad range of technical aspects (including perception, localization and navigation, motion control, etc.) and application domains (including automobile, aerospace, etc.), presents major challenges and discusses possible solutions.