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Decentralizing Finance: How DeFi, Digital Assets, and Distributed Ledger Technology Are Transforming Finance
by Kenneth BokA Practitioner's Guide to Decentralized Finance (DeFi), Digital Assets, and Distributed Ledger Technology In Decentralizing Finance: How DeFi, Digital Assets and Distributed Ledger Technology Are Transforming Finance, blockchain and digital assets expert Kenneth Bok offers an insightful exploration of the current state of decentralized finance (DeFi). As distributed ledger technology (DLT) increasingly optimizes and democratizes financial ecosystems worldwide, this book serves as a comprehensive guide to the most salient aspects of the ongoing transformation. The text delves into both crypto-native DeFi and DLT applications in regulated financial markets, providing: Comprehensive analysis of crypto-native DeFi across key areas such as its competitive landscape, infrastructure, financial instruments, activities, and applications Coverage of key risks, mitigation strategies, and regulatory frameworks, analyzed through the perspective of international financial standard-setting bodies Insight into how DLT is reshaping traditional financial systems through innovations like central bank digital currencies (CBDCs), tokenized assets, tokenized deposits, and institutional-grade DeFi platforms In a world where financial technology is rewriting the fundamental code of digital currency, the future of money is undeniably DLT-centric. How will this seismic shift interact with existing financial infrastructures? Can decentralization and traditional banking coexist and potentially synergize? This book endeavors to answer these pressing questions for financial professionals navigating these transformative times. Authored by a former Goldman Sachs trader, past Head of Growth at Zilliqa, and an early Ethereum investor with extensive experience in both traditional finance and the crypto ecosystem, Decentralizing Finance provides you with an insider's perspective on the revolution that is DeFi.
Deception and Delay in Organized Conflict: Essays on the Mathematical Theory of Maskirovka (SpringerBriefs in Applied Sciences and Technology)
by Rodrick WallaceThis book explores the role of deception, delay, and self-deception in the dynamics of organized conflict, taking a formal approach that hews closely to the asymptotic limit theorems of information and control theories. The resulting probability models can, with some effort—and some confidence—be converted to statistical tools for the analysis of real-time observational and ‘experimental’ data on institutionalized confrontation across both traditional and emerging ‘Clausewitz Landscapes’.
Deception in Autonomous Transport Systems: Threats, Impacts and Mitigation Policies (Wireless Networks)
by Simon Parkinson Mauro Vallati Alexandros NikitasThis book provides a comprehensive overview of deception in autonomous transport systems. This involves investigating the threats facing autonomous transport systems and how they can contribute towards a deceptive attack, followed by their potential impact if successful, and finally, how they can be mitigated. The work in this book is grouped into three parts. This first part focuses on the area of smart cities, policies, and ethics. This includes critically appraising the trade-off between functionality and security with connected and autonomous vehicles. The second discusses a range of AI applications in the wider field of smart transport and mobility, such as detecting anomalies in vehicle behaviour to investigating detecting disobedient vehicles. Finally, the third part presents and discusses cybersecurity-related aspects to consider when dealing with Connected and Autonomous Vehicles (CAVs) and smart urban infrastructure. This includes analysing different attacks to investigating secure communication technologies. CAVs are a game-changing technology with the potential to transform the way transport is perceived, mobility is serviced, travel ecosystems ‘behave’, and cities and societies as a whole function. There are many foreseen safety, accessibility and sustainability benefits resulting from the adoption of CAVs because of their ability, in theory, to operate error-free and collaboratively, ranging from accident prevention, congestion reduction and decreased carbon emissions to time savings, increased social inclusion, optimised routing, and better traffic control. However, no matter what the expected benefits are, CAVs are at the same time susceptible to an unprecedented number of new digital and physical threats. The severity of these threats has resulted in an increased effort to deepen our understanding of CAVs when it comes to their safety and resilience. In this complex and multi-faceted scenario, this book aims to provide an extensive overview of the risks related to the malicious exploitation of CAVs and beyond, the potential ways in which vulnerabilities can be exploited, prevention and mitigation policies and techniques, and the impact that the non-acceptance of Connected and Autonomous Mobility can have on the Smart City agenda. This book targets researchers, practitioners, and advanced-level students in computer science and transport engineering.
Deceptive AI: First International Workshop, DeceptECAI 2020, Santiago de Compostela, Spain, August 30, 2020 and Second International Workshop, DeceptAI 2021, Montreal, Canada, August 19, 2021, Proceedings (Communications in Computer and Information Science #1296)
by Benjamin Wright Stefan Sarkadi Peta Masters Peter McBurneyThis book constitutes selected papers presented at the First International Workshop on Deceptive AI, DeceptECAI 2020, held in conjunction with the 24th European Conference on Artificial Intelligence, ECAI 2020, in Santiago de Compostela, Spain, in August 2020, and Second International Workshop on Deceptive AI, DeceptAI 2021, held in conjunction with the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021, in Montreal, Canada, in August 2021. Due to the COVID-19 pandemic both conferences were held in a virtual mode. The 12 papers presented were thoroughly reviewed and selected from the 16 submissions. They present recent developments in the growing area of research in the interface between deception and AI.
Decide Better: Open and Interoperable Local Digital Twins
by Martin Brynskov Lieven Raes Susie Ruston McAleer Ingrid Croket Pavel Kogut Stefan LefeverThis is an open access book. Decide Better: Open and Interoperable Local Digital Twins explores the transformative potential of Local Digital Twins (LDTs) in urban governance. The book begins by introducing the concept of LDTs, which create digital replicas of cities or regions, combining real-time data and simulations to inform decision-making. It highlights how LDTs can enhance urban management by fostering collaboration among stakeholders and providing evidence-based insights for policy and operational strategies. The book emphasises the importance of openness, interoperability, and ethical use in LDT development, offering practical guidance for policymakers, urban planners, and technologists. The book is divided into three parts. The first part discusses the foundational principles of LDTs and their role in making cities smarter through data-driven decision-making. The second part focuses on implementing reusable LDT architectures, emphasising standards and interoperability. The final section addresses maximising LDT impact, offering strategies for governance, scalability, and ethical considerations. Drawing from real-world examples and expert insights, the book provides a comprehensive framework for adopting LDTs in diverse urban environments, aiming to advance sustainable and citizen-centric urban development.
Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh
by James SerraData fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of these architectures to help data professionals understand the pros and cons of each. James Serra, big data and data warehousing solution architect at Microsoft, examines common data architecture concepts, including how data warehouses have had to evolve to work with data lake features. You'll learn what data lakehouses can help you achieve, as well as how to distinguish data mesh hype from reality. Best of all, you'll be able to determine the most appropriate data architecture for your needs. With this book, you'll:Gain a working understanding of several data architecturesLearn the strengths and weaknesses of each approachDistinguish data architecture theory from realityPick the best architecture for your use caseUnderstand the differences between data warehouses and data lakesLearn common data architecture concepts to help you build better solutionsExplore the historical evolution and characteristics of data architecturesLearn essentials of running an architecture design session, team organization, and project success factorsFree from product discussions, this book will serve as a timeless resource for years to come.
Deciphering Object-Oriented Programming with C++: A practical, in-depth guide to implementing object-oriented design principles to create robust code
by Dorothy R. KirkEmbrace object-oriented programming and explore language complexities, design patterns, and smart programming techniques using this hands-on guide with C++ 20 compliant examplesKey FeaturesApply object-oriented design concepts in C++ using direct language features and refined programming techniquesDiscover sophisticated programming solutions with nuances to become an efficient programmerExplore design patterns as proven solutions for writing scalable and maintainable C++ softwareBook DescriptionEven though object-oriented software design enables more easily maintainable code, companies choose C++ as an OO language for its speed. Object-oriented programming in C++ is not automatic – it is crucial to understand OO concepts and how they map to both C++ language features and OOP techniques. Distinguishing your code by utilizing well-tested, creative solutions, which can be found in popular design patterns, is crucial in today's marketplace. This book will help you to harness OOP in C++ to write better code.Starting with the essential C++ features, which serve as building blocks for the key chapters, this book focuses on explaining fundamental object-oriented concepts and shows you how to implement them in C++. With the help of practical code examples and diagrams, you'll learn how and why things work. The book's coverage furthers your C++ repertoire by including templates, exceptions, operator overloading, STL, and OO component testing. You'll discover popular design patterns with in-depth examples and understand how to use them as effective programming solutions to solve recurring OOP problems.By the end of this book, you'll be able to employ essential and advanced OOP concepts to create enduring and robust software.What you will learnQuickly learn core C++ programming skills to develop a base for essential OOP features in C++Implement OO designs using C++ language features and proven programming techniquesUnderstand how well-designed, encapsulated code helps make more easily maintainable softwareWrite robust C++ code that can handle programming exceptionsDesign extensible and generic code using templatesApply operator overloading, utilize STL, and perform OO component testingExamine popular design patterns to provide creative solutions for typical OO problemsWho this book is forProgrammers wanting to utilize C++ for OOP will find this book essential to understand how to implement OO designs in C++ through both language features and refined programming techniques while creating robust and easily maintainable code. This OOP book assumes prior programming experience; however, if you have limited or no prior C++ experience, the early chapters will help you learn essential C++ skills to serve as the basis for the many OOP sections, advanced features, and design patterns.
Decision Analytics Applications in Industry (Asset Analytics)
by Uday Kumar P. K. Kapur Gurinder Singh Yury S. KlochkovThis book presents a range of qualitative and quantitative analyses in areas such as cybersecurity, sustainability, multivariate analysis, customer satisfaction, parametric programming, software reliability growth modeling, and blockchain technology, to name but a few. It also highlights integrated methods and practices in the areas of machine learning and genetic algorithms. After discussing applications in supply chains and logistics, cloud computing, six sigma, production management, big data analysis, satellite imaging, game theory, biometric systems, quality, and system performance, the book examines the latest developments and breakthroughs in the field of science and technology, and provides novel problem-solving methods. The themes discussed in the book link contributions by researchers and practitioners from different branches of engineering and management, and hailing from around the globe. These contributions provide scholars with a platform to derive maximum utility in the area of analytics by subscribing to the idea of managing business through system sciences, operations, and management. Managers and decision-makers can learn a great deal from the respective chapters, which will help them devise their own business strategies and find real-world solutions to complex industrial problems.
Decision Analytics for Sustainable Development in Smart Society 5.0: Issues, Challenges and Opportunities (Asset Analytics)
by Vishal Bhatnagar Joan Lu Vikram Bali Kakoli BanerjeeThis book covers sustainable development in smart society’s 5.0 using data analytics. The data analytics is the approach of integrating diversified heterogeneous data for predictive analysis to accredit innovation, decision making, business analysis, and strategic decision making. The data science brings together the research in the field of data analytics, online information analytics, and big data analytics to synthesize issues, challenges, and opportunities across smart society 5.0. Accordingly, the book offers an interesting and insightful read for researchers in the areas of decision analytics, cognitive analytics, big data analytics, visual analytics, text analytics, spatial analytics, risk analytics, graph analytics, predictive analytics, and analytics-enabled applications.
Decision Economics. Designs, Models, and Techniques for Boundedly Rational Decisions (Advances in Intelligent Systems and Computing #805)
by Shu-Heng Chen Juan Manuel Corchado Edgardo BucciarelliThe special session on Decision Economics (DECON) is a scientific forum held annually, which is focused on sharing ideas, projects, research results, models, and experiences associated with the complexity of behavioural decision processes and socio‐economic phenomena. In 2018, DECON was held at Campus Tecnológico de la Fábrica de Armas, University of Castilla-La Mancha, Toledo, Spain, as part of the 15th International Conference on Distributed Computing and Artificial Intelligence. For the third consecutive year, this book have drawn inspiration from Herbert A. Simon’s interdisciplinary legacy and, in particular, is devoted to designs, models, and techniques for boundedly rational decisions, involving several fields of study and expertise. It is worth noting that the recognition of relevant decision‐making takes place in a range of critical subject areas and research fields, including economics, finance, information systems, small and international business management, operations, and production. Therefore, decision‐making issues are of fundamental importance in all branches of economics addressed with different methodological approaches. As a matter of fact, the study of decision‐making has become the focus of intense research efforts, both theoretical and applied, forming a veritable bridge between theory and practice as well as science and business organisations, whose pillars are based on insightful cutting‐edge experimental, behavioural, and computational approaches on the one hand, and celebrating the value of science as well as the close relationship between economics and complexity on the other. In this respect, the international scientific community acknowledges Herbert A. Simon’s research endeavours to understand the processes involved in economic decision‐making and their implications for the advancement of economic professions. Within the field of decision‐making, indeed, Simon has become a mainstay of bounded rationality and satisficing. His rejection of the standard (unrealistic) decision‐making models adopted by neoclassical economists inspired social scientists worldwide with the purpose to develop research programmes aimed at studying decision‐making empirically, experimentally, and computationally. The main achievements concern decision‐making for individuals, firms, markets, governments, institutions, and, last but not least, science and research. This book of selected papers tackles these issues that Simon broached in a professional career spanning more than sixty years. The Editors of this book dedicated it to Herb.
Decision Economics: Complexity of Decisions and Decisions for Complexity (Advances in Intelligent Systems and Computing #1009)
by Shu-Heng Chen Juan Manuel Corchado Edgardo BucciarelliThis book is based on the International Conference on Decision Economics (DECON 2019). Highlighting the fact that important decision-making takes place in a range of critical subject areas and research fields, including economics, finance, information systems, psychology, small and international business, management, operations, and production, the book focuses on analytics as an emerging synthesis of sophisticated methodology and large data systems used to guide economic decision-making in an increasingly complex business environment.DECON 2019 was organised by the University of Chieti-Pescara (Italy), the National Chengchi University of Taipei (Taiwan), and the University of Salamanca (Spain), and was held at the Escuela politécnica Superior de Ávila, Spain, from 26th to 28th June, 2019. Sponsored by IEEE Systems Man and Cybernetics Society, Spain Section Chapter, and IEEE Spain Section (Technical Co-Sponsor), IBM, Indra, Viewnext, Global Exchange, AEPIA-and-APPIA, with the funding supporting of the Junta de Castilla y León, Spain (ID: SA267P18-Project co-financed with FEDER funds)
Decision Economics: Minds, Machines, and their Society (Studies in Computational Intelligence #990)
by Shu-Heng Chen Edgardo Bucciarelli Juan M. Corchado Javier Parra D.This book is the result of a multi-year research project led and sponsored by the University of Chieti-Pescara, National Chengchi University, University of Salamanca, and Osaka University. It is the fifth volume to emerge from that international project, held under the aegis of the United Nations Academic Impact in 2020. All the essays in this volume were (virtually) discussed at the University of L’Aquila―as the venue of the 2nd International Conference on Decision Economics, a three-day global gathering of approximately one hundred scholars and practitioners—and were subjected to thorough peer review by leading experts in the field. The essays reflect the extent, diversity, and richness of several research areas, both normative and descriptive, and are an invaluable resource for graduate-level and PhD students, academics, researchers, policymakers and other professionals, especially in the social and cognitive sciences. Given its interdisciplinary scope, the book subsequently delivers new approaches on how to contribute to the future of economics, providing alternative explanations for various socio-economic issues such as computable humanities; cognitive, behavioural, and experimental perspectives in economics; data analysis and machine learning as well as research areas at the intersection of computer science, artificial intelligence, mathematics, and statistics; agent-based modelling and the related. The editors are grateful to the scientific committee for its continuous support throughout the research project as well as to the many participants for their insightful comments and always probing questions. In any case, the collaboration involved in the project extends far beyond the group of authors published in this volume and is reflected in the quality of the essays published over the years.
Decision Forests for Computer Vision and Medical Image Analysis
by Antonio Criminisi J ShottonThis practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification; includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website; provides a free, user-friendly software library, enabling the reader to experiment with forests in a hands-on manner.
Decision Intelligence Analytics and the Implementation of Strategic Business Management (EAI/Springer Innovations in Communication and Computing)
by Dieu Hack-Polay Tanupriya Choudhury P. Mary Jeyanthi T P Singh Sheikh AbujarThis book presents a framework for developing an analytics strategy that includes a range of activities, from problem definition and data collection to data warehousing, analysis, and decision making. The authors examine best practices in team analytics strategies such as player evaluation, game strategy, and training and performance. They also explore the way in which organizations can use analytics to drive additional revenue and operate more efficiently. The authors provide keys to building and organizing a decision intelligence analytics that delivers insights into all parts of an organization. The book examines the criteria and tools for evaluating and selecting decision intelligence analytics technologies and the applicability of strategies for fostering a culture that prioritizes data-driven decision making. Each chapter is carefully segmented to enable the reader to gain knowledge in business intelligence, decision making and artificial intelligence in a strategic management context.
Decision Intelligence For Dummies
by Pamela BakerLearn to use, and not be used by, data to make more insightful decisions The availability of data and various forms of AI unlock countless possibilities for business decision makers. But what do you do when you feel pressured to cede your position in the decision-making process altogether? Decision Intelligence For Dummies pumps the brakes on the growing trend to take human beings out of the decision loop and walks you through the best way to make data-informed but human-driven decisions. The book shows you how to achieve maximum flexibility by using every available resource, and not just raw data, to make the most insightful decisions possible. In this timely book, you’ll learn to: Make data a means to an end, rather than an end in itself, by expanding your decision-making inquiries Find a new path to solid decisions that includes, but isn’t dominated, by quantitative data Measure the results of your new framework to prove its effectiveness and efficiency and expand it to a whole team or company Perfect for business leaders in technology and finance, Decision Intelligence For Dummies is ideal for anyone who recognizes that data is not the only powerful tool in your decision-making toolbox. This book shows you how to be guided, and not ruled, by the data.
Decision Intelligence Solutions: Proceedings of the International Conference on Information Technology, InCITe 2023, Volume 2 (Lecture Notes in Electrical Engineering #1080)
by Manju Khari Seán McLoone Nitasha Hasteer Purushottam SharmaThis book comprises the select peer-reviewed proceedings of the 3rd International Conference on Information Technology (InCITe-2023). It aims to provide a comprehensive and broad-spectrum picture of state-of-the-art research and development in decision intelligence, deep learning, machine learning, artificial intelligence, data science, and enabling technologies for IoT, blockchain, and other futuristic computational technologies. It covers various topics that span cutting-edge, collaborative technologies and areas of computation. The content would serve as a rich knowledge repository on information & communication technologies, neural networks, fuzzy systems, natural language processing, data mining & warehousing, big data analytics, cloud computing, security, social networks and intelligence, decision-making and modeling, information systems, and IT architectures. This book provides a valuable resource for those in academia and industry.
Decision Intelligence: Human–Machine Integration for Decision-Making
by Miriam O'CallaghanRevealing the limitations of human decision-making, this book explores how Artificial Intelligence (AI) can be used to optimize decisions for improved business outcomes and efficiency, as well as looking ahead to the significant contributions Decision Intelligence (DI) can make to society and the ethical challenges it may raise. From the theories and concepts used to design autonomous intelligent agents to the technologies that power DI systems and the ways in which companies use decision-making building blocks to build DI solutions that enable businesses to democratize AI, this book presents an impressive framework to integrate artificial and human intelligence for the success of different types of business decisions. Replete with case studies on DI applications, as well as wider discussions on the social implications of the technology, Decision Intelligence: Human–Machine Integration for Decision Making appeals to both students of AI and data sciences and businesses considering DI adoption.
Decision Intelligence: Proceedings of the International Conference on Information Technology, InCITe 2023, Volume 1 (Lecture Notes in Electrical Engineering #1079)
by B. K. Murthy Nitasha Hasteer Jean-Paul Van Belle B. V. R. ReddyThis book comprises the select peer-reviewed proceedings of the 3rd International Conference on Information Technology (InCITe-2023). It aims to provide a comprehensive and broad-spectrum picture of state-of-the-art research and development in decision intelligence, deep learning, machine learning, artificial intelligence, data science, and enabling technologies for IoT, blockchain, and other futuristic computational technologies. It covers various topics that span cutting-edge, collaborative technologies and areas of computation. The content would serve as a rich knowledge repository on information & communication technologies, neural networks, fuzzy systems, natural language processing, data mining & warehousing, big data analytics, cloud computing, security, social networks, and intelligence, decision-making, and modeling, information systems, and IT architectures. This book provides a valuable resource for those in academia and industry.
Decision Making And Problem Solving: A Practical Guide For Applied Research
by Sachi Nandan MohantyIn Decision Making and Problem Solving: A Practical Guide for Applied Research, the author utilizes traditional approaches, tools, and techniques adopted to solve current day-to-day, real-life problems. The book offers guidance in identifying and applying accurate methods for designing a strategy as well as implementing these strategies in the real world. The book includes realistic case studies and practical approaches that should help readers understand how the decision making occurs and can be applied to problem solving under deep uncertainty.
Decision Making Under Uncertainty Via Optimization, Modelling, and Analysis (Studies in Systems, Decision and Control #558)
by Ronald R. Yager Laxminarayan Sahoo Madhumangal Pal Tapan SenapatiThis book focuses on cutting-edge developments in optimal decision-making incorporating modeling and optimization for determining renewable energy sources, supply chain management, and environmental planning under uncertainty. It addresses mathematical models of cost-effective management policies. This book presents the best decision-making practices for solving real-world challenges. This book provides access to an invaluable collection of various decision-making issues that scholars and industry practitioners use as a reference. The readers are able to understand how decision-making problems are formulated under uncertainty and how to use right optimization strategies to fix problems.
Decision Making Under Uncertainty and Constraints: A Why-Book (Studies in Systems, Decision and Control #217)
by Vladik Kreinovich Martine CeberioThis book shows, on numerous examples, how to make decisions in realistic situations when we have both uncertainty and constraints. In most these situations, the book's emphasis is on the why-question, i.e., on a theoretical explanation for empirical formulas and techniques. Such explanations are important: they help understand why these techniques work well in some cases and not so well in others, and thus, help practitioners decide whether a technique is appropriate for a given situation. Example of applications described in the book ranges from science (biosciences, geosciences, and physics) to electrical and civil engineering, education, psychology and decision making, and religion—and, of course, include computer science, AI (in particular, eXplainable AI), and machine learning. The book can be recommended to researchers and students in these application areas. Many of the examples use general techniques that can be used in other application areas as well, so it is also useful for practitioners and researchers in other areas who are looking for possible theoretical explanations of empirical formulas and techniques.
Decision Making Under Uncertainty and Reinforcement Learning: Theory and Algorithms (Intelligent Systems Reference Library #223)
by Ronald Ortner Christos DimitrakakisThis book presents recent research in decision making under uncertainty, in particular reinforcement learning and learning with expert advice. The core elements of decision theory, Markov decision processes and reinforcement learning have not been previously collected in a concise volume. Our aim with this book was to provide a solid theoretical foundation with elementary proofs of the most important theorems in the field, all collected in one place, and not typically found in introductory textbooks. This book is addressed to graduate students that are interested in statistical decision making under uncertainty and the foundations of reinforcement learning.
Decision Making Under Uncertainty, with a Special Emphasis on Geosciences and Education (Studies in Systems, Decision and Control #218)
by Vladik Kreinovich Laxman BokatiThis book describes new techniques for making decisions in situations with uncertainty and new applications of decision-making techniques. The main emphasis is on situations when it is difficult to decrease uncertainty. For example, it is very difficult to accurately predict human economic behavior, so in economics, it is very important to take this uncertainty into account when making decisions. Other areas where it is difficult to decrease uncertainty are geosciences and teaching. The book analyzes the general problem of decision making and shows how its results can be applied to economics, geosciences, and teaching. Since all these applications involve computing, the book also shows how these results can be applied to computing, including deep learning and quantum computing. The book is recommended to researchers, practitioners, and students who want to learn more about decision making under uncertainty—and who want to work on remaining challenges.
Decision Making Under Uncertainty: Theory and Application (MIT Lincoln Laboratory Series)
by Mykel J. KochenderferAn 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.
Decision Making Using AI in Energy and Sustainability: Methods and Models for Policy and Practice (Applied Innovation and Technology Management)
by Gülgün Kayakutlu M. Özgür KayalicaArtificial intelligence (AI) has a huge impact on science and technology, including energy, where access to resources has been a source of geopolitical conflicts. AI can predict the demand and supply of renewable energy, optimize efficiency in energy systems, and improve the management of natural energy resources, among other things. This book explores the use of AI tools for improving the management of energy systems and providing sustainability with smart cities, smart facilities, smart buildings, smart transportation, and smart houses. Featuring research from International Federation for Information Processing's (IFIP) “AI in Energy and Sustainability” working group, this book provides new models and algorithms for AI applications in energy and sustainability fields. Any short-term, mid-term and long-term forecasting, optimization models, trend foresights and prescriptions based on scenarios are studied in the energy world and the smart systems for sustainability. The contents of this book are valuable for energy researchers, academics, scholars, practitioners and policy makers.