Download or read book Dynamic Execution of Temporal Plans with Sensing Actions and Bounded Risk written by Pedro Henrique de Rodrigues Quemel e Assis Santana and published by . This book was released on 2016 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: A special report on the cover of the June 2016 issue of the IEEE Spectrum magazine reads: "can we trust robots?" In a world that has been experiencing a seemingly irreversible process by which autonomous systems have been given increasingly more space in strategic areas such as transportation, manufacturing, energy supply, planetary exploration, and even medical surgeries, it is natural that we start asking ourselves if these systems could be held at the same or even higher levels of safety than we expect from humans. In an effort to make a contribution towards a world of autonomy that we can trust, this thesis argues that one necessary step in this direction is the endowment of autonomous agents with the ability to dynamically adapt to their environment while meeting strict safety guarantees. From a technical standpoint, we propose that autonomous agents in safety-critical applications be able to execute conditional plans (or policies) within risk bounds (also referred to as chance constraints). By being conditional, the plan allows the autonomous agent to adapt to its environment in real-time by conditioning the choice of activity to be executed on the agent's current level of knowledge, or belief, about the true state of world. This belief state is, in turn, a function of the history of potentially noisy sensor observations gathered by the agent from the environment. With respect to bounded risk, it refers to the fact that executing such conditional plans should guarantee to keep the agent "safe" - as defined by sets of state constraints - with high probability, while moving away from the conservatism of minimum risk approaches. In this thesis, we propose Chance-Constrained Partially Observable Markov Decision Processes (CC-POMDP's) as a formalism for conditional risk-bounded planning under uncertainty. Moreover, we present Risk-bounded AO* (RAO*), a heuristic forward search-based algorithm that searches for solutions to a CC-POMDP by leveraging admissible utility and risk heuristics to simultaneously guide the search and perform early pruning of overly-risky policy branches. In an effort to facilitate the specification of risk-bounded behavior by human modelers, we also present the Chance-constrained Reactive Model-based Programming Language (cRMPL), a novel variant of RMPL that incorporates chance constraints as part of its syntax. Finally, in support of the temporal planning applications with duration uncertainty that this thesis is concerned about, we present the Polynomial-time Algorithm for Risk-aware Scheduling (PARIS) and its extension to conditional scheduling of Probabilistic Temporal Plan Networks (PTPN's). The different tools and algorithms developed in the context of this thesis are combined to form the Conditional Planning for Autonomy with Risk (CLARK) system, a risk-aware conditional planning system that can generate chance-constrained, dynamic temporal plans for autonomous agents that must operate under uncertainty. With respect to our empirical validation, each component of CLARK is benchmarked against the relevant state of the art throughout the chapters, followed by several demonstrations of the whole CLARK system working in tandem with other building blocks of an architecture for autonomy.
Download or read book An Introduction to Constraint Based Temporal Reasoning written by Roman Barták and published by Morgan & Claypool Publishers. This book was released on 2014-02-01 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solving challenging computational problems involving time has been a critical component in the development of artificial intelligence systems almost since the inception of the field. This book provides a concise introduction to the core computational elements of temporal reasoning for use in AI systems for planning and scheduling, as well as systems that extract temporal information from data. It presents a survey of temporal frameworks based on constraints, both qualitative and quantitative, as well as of major temporal consistency techniques. The book also introduces the reader to more recent extensions to the core model that allow AI systems to explicitly represent temporal preferences and temporal uncertainty. This book is intended for students and researchers interested in constraint-based temporal reasoning. It provides a self-contained guide to the different representations of time, as well as examples of recent applications of time in AI systems.
Download or read book Automated Planning and Acting written by Malik Ghallab and published by Cambridge University Press. This book was released on 2016-08-09 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the most recent and advanced techniques for creating autonomous AI systems capable of planning and acting effectively.
Download or read book On the Move to Meaningful Internet Systems OTM 2012 written by Robert Meersman and published by Springer. This book was released on 2013-01-17 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 7565 and 7566 constitutes the refereed proceedings of three confederated international conferences: Cooperative Information Systems (CoopIS 2012), Distributed Objects and Applications - Secure Virtual Infrastructures (DOA-SVI 2012), and Ontologies, DataBases and Applications of SEmantics (ODBASE 2012) held as part of OTM 2012 in September 2012 in Rome, Italy. The 53 revised full papers presented were carefully reviewed and selected from a total of 169 submissions. The 22 full papers included in the first volume constitute the proceedings of CoopIS 2012 and are organized in topical sections on business process design; process verification and analysis; service-oriented architectures and cloud; security, risk, and prediction; discovery and detection; collaboration; and 5 short papers.
Download or read book Autonomous Horizons written by Greg Zacharias and published by Independently Published. This book was released on 2019-04-05 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr. Greg Zacharias, former Chief Scientist of the United States Air Force (2015-18), explores next steps in autonomous systems (AS) development, fielding, and training. Rapid advances in AS development and artificial intelligence (AI) research will change how we think about machines, whether they are individual vehicle platforms or networked enterprises. The payoff will be considerable, affording the US military significant protection for aviators, greater effectiveness in employment, and unlimited opportunities for novel and disruptive concepts of operations. Autonomous Horizons: The Way Forward identifies issues and makes recommendations for the Air Force to take full advantage of this transformational technology.
Download or read book Reliability Risk and Safety Three Volume Set written by Radim Bris and published by CRC Press. This book was released on 2009-08-20 with total page 2480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Containing papers presented at the 18th European Safety and Reliability Conference (Esrel 2009) in Prague, Czech Republic, September 2009, Reliability, Risk and Safety Theory and Applications will be of interest for academics and professionals working in a wide range of industrial and governmental sectors, including Aeronautics and Aerospace, Aut
Download or read book Constrained Markov Decision Processes written by Eitan Altman and published by Routledge. This book was released on 2021-12-17 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unified approach for the study of constrained Markov decision processes with a finite state space and unbounded costs. Unlike the single controller case considered in many other books, the author considers a single controller with several objectives, such as minimizing delays and loss, probabilities, and maximization of throughputs. It is desirable to design a controller that minimizes one cost objective, subject to inequality constraints on other cost objectives. This framework describes dynamic decision problems arising frequently in many engineering fields. A thorough overview of these applications is presented in the introduction. The book is then divided into three sections that build upon each other.
Download or read book Probability Theory written by and published by Allied Publishers. This book was released on 2013 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability theory
Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Download or read book Patterns Predictions and Actions Foundations of Machine Learning written by Moritz Hardt and published by Princeton University Press. This book was released on 2022-08-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers
Download or read book Reinforcement Learning and Dynamic Programming Using Function Approximators written by Lucian Busoniu and published by CRC Press. This book was released on 2017-07-28 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade. In its pages, pioneering experts provide a concise introduction to classical RL and DP, followed by an extensive presentation of the state-of-the-art and novel methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, they elaborate on their work with illustrative examples and insightful comparisons. Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy search. The features and performance of these algorithms are highlighted in extensive experimental studies on a range of control applications. The recent development of applications involving complex systems has led to a surge of interest in RL and DP methods and the subsequent need for a quality resource on the subject. For graduate students and others new to the field, this book offers a thorough introduction to both the basics and emerging methods. And for those researchers and practitioners working in the fields of optimal and adaptive control, machine learning, artificial intelligence, and operations research, this resource offers a combination of practical algorithms, theoretical analysis, and comprehensive examples that they will be able to adapt and apply to their own work. Access the authors' website at www.dcsc.tudelft.nl/rlbook/ for additional material, including computer code used in the studies and information concerning new developments.
Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Download or read book Automated Planning written by Malik Ghallab and published by Elsevier. This book was released on 2004-05-03 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description
Download or read book Exploring the Feasibility and Utility of Machine Learning Assisted Command and Control written by Matthew Walsh and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume serves as the technical analysis to a report concerning the potential for artificial intelligence systems to assist in Air Force command and control (C2). The authors present their analysis of the problem characteristics and solution capabilities described in Volume 1. They provide details about the expert panel used to gather data and three technical case studies that demonstrate a wide range of solutions to various C2 problems.
Download or read book Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation written by Intergovernmental Panel on Climate Change and published by Cambridge University Press. This book was released on 2012-05-28 with total page 593 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extreme weather and climate events, interacting with exposed and vulnerable human and natural systems, can lead to disasters. This Special Report explores the social as well as physical dimensions of weather- and climate-related disasters, considering opportunities for managing risks at local to international scales. SREX was approved and accepted by the Intergovernmental Panel on Climate Change (IPCC) on 18 November 2011 in Kampala, Uganda.
Download or read book Autonomous Vehicles in Support of Naval Operations written by National Research Council and published by National Academies Press. This book was released on 2005-08-05 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles (AVs) have been used in military operations for more than 60 years, with torpedoes, cruise missiles, satellites, and target drones being early examples.1 They have also been widely used in the civilian sector-for example, in the disposal of explosives, for work and measurement in radioactive environments, by various offshore industries for both creating and maintaining undersea facilities, for atmospheric and undersea research, and by industry in automated and robotic manufacturing. Recent military experiences with AVs have consistently demonstrated their value in a wide range of missions, and anticipated developments of AVs hold promise for increasingly significant roles in future naval operations. Advances in AV capabilities are enabled (and limited) by progress in the technologies of computing and robotics, navigation, communications and networking, power sources and propulsion, and materials. Autonomous Vehicles in Support of Naval Operations is a forward-looking discussion of the naval operational environment and vision for the Navy and Marine Corps and of naval mission needs and potential applications and limitations of AVs. This report considers the potential of AVs for naval operations, operational needs and technology issues, and opportunities for improved operations.
Download or read book Motion Planning in Dynamic Environments written by Kikuo Fujimura and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer Science Workbench is a monograph series which will provide you with an in-depth working knowledge of current developments in computer technology. Every volume in this series will deal with a topic of importance in computer science and elaborate on how you yourself can build systems related to the main theme. You will be able to develop a variety of systems, including computer software tools, computer graphics, computer animation, database management systems, and computer-aided design and manufacturing systems. Computer Science Workbench represents an important new contribution in the field of practical computer technology. TOSIYASU L. KUNII To my parents Kenjiro and Nori Fujimura Preface Motion planning is an area in robotics that has received much attention recently. Much of the past research focuses on static environments - various methods have been developed and their characteristics have been well investigated. Although it is essential for autonomous intelligent robots to be able to navigate within dynamic worlds, the problem of motion planning in dynamic domains is relatively little understood compared with static problems.
