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Decision-Based Design
by Christopher Hoyle Henk Jan Wassenaar Wei ChenBuilding upon the fundamental principles of decision theory, Decision-Based Design: Integrating Consumer Preferences into Engineering Design presents an analytical approach to enterprise-driven Decision-Based Design (DBD) as a rigorous framework for decision making in engineering design. Once the related fundamentals of decision theory, economic analysis, and econometrics modelling are established, the remaining chapters describe the entire process, the associated analytical techniques, and the design case studies for integrating consumer preference modeling into the enterprise-driven DBD framework. Methods for identifying key attributes, optimal design of human appraisal experiments, data collection, data analysis, and demand model estimation are presented and illustrated using engineering design case studies. The scope of the chapters also provides: A rigorous framework of integrating the interests from both producer and consumers in engineering design, Analytical techniques of consumer choice modelling to forecast the impact of engineering decisions, Methods for synthesizing business and engineering models in multidisciplinary design environments, and Examples of effective application of Decision-Based Design supported by case studies. No matter whether you are an engineer facing decisions in consumer related product design, an instructor or student of engineering design, or a researcher exploring the role of decision making and consumer choice modelling in design, Decision-Based Design: Integrating Consumer Preferences into Engineering Design provides a reliable reference over a range of key topics.
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. Designs, Models, and Techniques for Boundedly Rational Decisions (Advances in Intelligent Systems and Computing #805)
by Edgardo Bucciarelli Shu-Heng Chen Juan Manuel CorchadoThe 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: Minds, Machines, and their Society (Studies in Computational Intelligence #990)
by Edgardo Bucciarelli Shu-Heng Chen 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: Proceedings of the International Conference on Information Technology, InCITe 2023, Volume 1 (Lecture Notes in Electrical Engineering #1079)
by B. K. Murthy B. V. R. Reddy Nitasha Hasteer Jean-Paul Van BelleThis 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 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.
The Decision Intelligence Handbook
by L. Y. Pratt N. E. MalcolmDecision intelligence (DI) has been widely named as a top technology trend for several years, and Gartner reports that more than a third of large organizations are adopting it. Some even say that DI is the next step in the evolution of AI. Many software vendors offer DI solutions today, as they help organizations implement their evidence-based or data-driven decision strategies.But until now, there has been little practical guidance for organizations to formalize decision making and integrate their decisions with data.With this book, authors L. Y. Pratt and N. E. Malcolm fill this gap. They present a step-by-step method for integrating technology into decisions that bridge from actions to desired outcomes, with a focus on systems that act in an advisory, human-in-the-loop capacity to decision makers.This handbook addresses three widespread data-driven decision-making problems:How can decision makers use data and technology to ensure desired outcomes?How can technology teams communicate effectively with decision makers to maximize the return on their data and technology investments?How can organizational decision makers assess and improve their decisions over time?
Decision Intelligence Solutions: Proceedings of the International Conference on Information Technology, InCITe 2023, Volume 2 (Lecture Notes in Electrical Engineering #1080)
by Nitasha Hasteer Seán McLoone Manju Khari 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.
The Decision Maker's Handbook to Data Science: A Guide for Non-Technical Executives, Managers, and Founders
by Stylianos KampakisData science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. It is easy for novices to the subject to feel paralyzed by intimidating buzzwords, but what many don’t realize is that data science is in fact quite multidisciplinary—useful in the hands of business analysts, communications strategists, designers, and more.With the second edition of The Decision Maker’s Handbook to Data Science, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated and revised second edition, includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker’s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide.What You Will LearnUnderstand how data science can be used within your business.Recognize the differences between AI, machine learning, and statistics.Become skilled at thinking like a data scientist, without being one.Discover how to hire and manage data scientists.Comprehend how to build the right environment in order to make your organization data-driven. Who This Book Is For Startup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science.
Decision Making: Uncertainty, Imperfection, Deliberation and Scalability
by Tatiana V. Guy Miroslav Kárný David H. WolpertThis volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selsh decision makers. The chapters are written by leading experts from different disciplines, all considering the many sources of imperfection in decision making, and always with an eye to decreasing the myriad discrepancies between theory and real world human decision making. Topics addressed include uncertainty, deliberation cost and the complexity arising from the inherent large computational scale of decision making in these systems. In particular, analyses and experiments are presented which concern: * task allocation to maximize "the wisdom of the crowd"; * design of a society of "edutainment" robots who account for one anothers' emotional states; * recognizing and counteracting seemingly non-rational human decision making; * coping with extreme scale when learning causality in networks; * efciently incorporating expert knowledge in personalized medicine; * the effects of personality on risky decision making. The volume is a valuable source for researchers, graduate students and practitioners in machine learning, stochastic control, robotics, and economics, among other elds.
Decision-Making Analyses with Thermodynamic Parameters and Hesitant Fuzzy Linguistic Preference Relations (Studies in Fuzziness and Soft Computing #409)
by Peijia Ren Zeshui XuThe book introduces readers to some of the latest advances in and approaches to decision-making methods based on thermodynamic characters and hesitant fuzzy linguistic preference relations. By investigating the decision-making methods with thermodynamic parameters based on different information representatives, the book offers readers a novel perspective for solving problems under uncertainty. By exploring the consistency and consensus of hesitant fuzzy linguistic preference relations, the book gives readers efficient ways for preference analysis under uncertainty, chiefly intended for researchers and practitioners working in operations research, multi-attribute decision making, preference analysis, etc. The book can also be used as supplementary material for postgraduate and senior-year undergraduate students of the relevant professional institutions.
Decision-making Analysis and Optimization Modeling of Emergency Warnings for Major Accidents
by Wenmei Gai Yan Du Yunfeng DengThis book highlights cutting-edge research into emergency early warning management and decision-making for severe accidents. Using toxic gas leakages as examples, it puts forward new design methods for emergency early warning systems, as well as a systematic description of emergency early warning information communication mechanisms and characteristics of regional evacuation, based on a wide range of theories, including safety engineering, information engineering, communication, behaviorology and others. The book applies a range of methods, such as case analysis, questionnaire interviews, and multi-objective optimization modeling. Drawing on this basis, it subsequently proposes a multi-objective optimization modeling and algorithm for emergency path selection, together with an evacuation risk assessment method. Divided into six chapters prepared by an international team of researchers, the book addresses the design of early warning systems, communication and dissemination mechanisms of early warning information, characteristics of regional evacuation, multi-objective optimization of emergency paths, and evacuation risk assessment. The book offers an essential reference guide for engineering technicians and researchers in a wide range of fields, including emergency management, safety science and engineering, disaster relief engineering, and transportation optimization, as well as graduate students in related majors at colleges and universities.
Decision Making and Knowledge Decision Support Systems
by Anna Maria Gil-Lafuente Constantin ZopounidisThis book presents recent advancements of research, new methods and techniques, applications and projects in decision making and decision support systems. It explores expert systems and neural networks, knowledge engineering and management, fuzzy sets and systems and computational methods for optimization, data analysis and decision making. It presents applications in Economics, Finance, Management and Engineering. The book undertakes to stimulate scientific exchange, ideas and experiences in the field of decision making in Economy and Management. Researchers and practitioners alike will benefit from this book, when they are dealing with imprecision, vagueness and uncertainty in the context of decision making.
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 and Security Risk Management for IoT Environments (Advances in Information Security #106)
by Wadii Boulila Jawad Ahmad Anis Koubaa Maha Driss Imed Riadh FarahThis book contains contemporary research that outlines and addresses security, privacy challenges and decision-making in IoT environments. The authors provide a variety of subjects related to the following Keywords: IoT, security, AI, deep learning, federated learning, intrusion detection systems, and distributed computing paradigms. This book also offers a collection of the most up-to-date research, providing a complete overview of security and privacy-preserving in IoT environments. It introduces new approaches based on machine learning that tackles security challenges and provides the field with new research material that’s not covered in the primary literature. The Internet of Things (IoT) refers to a network of tiny devices linked to the Internet or other communication networks. IoT is gaining popularity, because it opens up new possibilities for developing many modern applications. This would include smart cities, smart agriculture, innovative healthcare services and more. The worldwide IoT market surpassed $100 billion in sales for the first time in 2017, and forecasts show that this number might reach $1.6 trillion by 2025. However, as IoT devices grow more widespread, threats, privacy and security concerns are growing. The massive volume of data exchanged highlights significant challenges to preserving individual privacy and securing shared data. Therefore, securing the IoT environment becomes difficult for research and industry stakeholders.Researchers, graduate students and educators in the fields of computer science, cybersecurity, distributed systems and artificial intelligence will want to purchase this book. It will also be a valuable companion for users and developers interested in decision-making and security risk management in IoT environments.
Decision-Making and the Information System
by Maryse SallesThe purpose of this book is to question the relationships involved in decision making and the systems designed to support it: decision support systems (DSS). The focus is on how these systems are engineered; to stop and think about the questions to be asked throughout the engineering process and, in particular, about the impact designers' choices have on these systems.
Decision-Making in Crisis Situations: Research and Innovation for Optimal Training
by Sophie SauvagnarguesThis book presents concepts and methods for optimal training for decision making in crisis situations. After presenting some general concepts of decision-making during crisis situations, it presents various innovations for optimal training, such as serious games, scenario design, adapted animation of crisis exercises, observation and debriefing of exercises related to pedagogical objectives.
Decision Making in Healthcare Systems (Studies in Systems, Decision and Control #513)
by Tofigh Allahviranloo Farhad Hosseinzadeh Lotfi Zohreh Moghaddas Mohsen Vaez-GhasemiThis book chooses the topic which is due to the editors' experience in modeling projects in healthcare systems. Also, the transfer of experiences is the reason why mathematical modeling and decision making in the field of health are not given much attention. To this end, the new aspect of this book is the lack of reference needed to carry out projects in the field of health for researchers whose main expertise is not modeling. Students of health, mathematics, management, and industrial engineering fields are in the direct readership with this book. Different projects in the field of healthcare systems can use the topics presented in different chapters mentioned in this book.
Decision Making in Social Sciences: Between Traditions and Innovations (Studies in Systems, Decision and Control #247)
by Daniel Flaut Šárka Hošková-Mayerová Cristina Ispas Fabrizio Maturo Cristina FlautThis book explores several branches of the social sciences and their perspectives regarding their relations with decision-making processes: computer science, education, linguistics, sociology, and management. The decision-making process in social contexts is based on the analysis of sound alternatives using evaluative criteria. Therefore, this process is one that can be rational or irrational, and can be based on knowledge and/or beliefs. A decision-making process always produces a final decision, which may or may not imply prompt action, and increases the chances of choosing the best possible alternative. The book is divided into four main parts. The concepts covered in the first part, on computer science, explore how the rise of algorithms and the growth in computing power over the years can influence decision-making processes. In the second part, some traditional and innovative ideas and methods used in education are presented: compulsory schooling, inclusive schools, higher education, etc. In turn, the third part focuses on linguistics aspects, and examines how progress is manifested in language. The fourth part, on sociology, explores how society can be influenced by social norms, human interactions, culture, and religion. Management, regarded as a science of the decision-making process, is explored in the last part of this book. Selected organizations’ strategies, objectives and resources are presented, e.g., human resources, financial resources, and technological resources. The book gathers and presents, in a concise format, a broad range of aspects regarding the decision-making process in social contexts, making it a valuable and unique resource for the scientific community.
Decision Making in Systems Engineering and Management (Wiley Series In Systems Engineering And Management Ser. #79)
by Patrick J DriscollDECISION MAKING IN SYSTEMS ENGINEERING AND MANAGEMENT A thoroughly updated overview of systems engineering management and decision making In the newly revised third edition of Decision Making in Systems Engineering and Management, the authors deliver a comprehensive and authoritative overview of the systems decision process, systems thinking, and qualitative and quantitative multi-criteria value modeling directly supporting decision making throughout the system lifecycle. This book offers readers major new updates that cover recently developed system modeling and analysis techniques and quantitative and qualitative approaches in the field, including effective techniques for addressing uncertainty. In addition to Excel, six new open-source software applications have been added to illustrate key topics, including SIPmath Modeler Tools, Cambridge Advanced Modeller, SystemiTool2.0, and Gephi 0.9.2. The authors have reshaped the book’s organization and presentation to better support educators engaged in remote learning. New appendices have been added to present extensions for a new realization analysis technique and getting started steps for each of the major software applications. Updated illustrative examples support modern system decision making skills and highlight applications in hardware, organizations, policy, logistic supply chains, and architecture. Readers will also find: Thorough introductions to working with systems, the systems engineering perspective, and systems thinking In-depth presentations of applied systems thinking, including holism, element dependencies, expansive and contractive thinking, and concepts of structure, classification, and boundaries Comprehensive explorations of system representations leading to analysis In-depth discussions of supporting system decisions, including the system decision process (SDP), tradespace methods, multi-criteria value modeling, working with stakeholders, and the system environment Perfect for undergraduate and graduate students studying systems engineering and systems engineering management, Decision Making in Systems Engineering and Management will also earn a place in the libraries of practicing system engineers and researchers with an interest in the topic.
Decision Making under Constraints (Studies in Systems, Decision and Control #276)
by Vladik Kreinovich Martine CeberioThis book presents extended versions of selected papers from the annual International Workshops on Constraint Programming and Decision Making from 2016 to 2018. The papers address all stages of decision-making under constraints: (1) precisely formulating the problem of multi-criteria decision-making; (2) determining when the corresponding decision problem is algorithmically solvable; (3) finding the corresponding algorithms and making these algorithms as efficient as possible; and (4) taking into account interval, probabilistic, and fuzzy uncertainty inherent in the corresponding decision-making problems. In many application areas, it is necessary to make effective decisions under constraints, and there are several area-specific techniques for such decision problems. However, because they are area-specific, it is not easy to apply these techniques in other application areas. As such, the annual International Workshops on Constraint Programming and Decision Making focus on cross-fertilization between different areas, attracting researchers and practitioners from around the globe. The book includes numerous papers describing applications, in particular, applications to engineering, such as control of unmanned aerial vehicles, and vehicle protection against improvised explosion devices.
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.