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Decision Making Under Uncertainty And Reinforcement Learning
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Book Synopsis Decision Making Under Uncertainty and Reinforcement Learning by : Christos Dimitrakakis
Download or read book Decision Making Under Uncertainty and Reinforcement Learning written by Christos Dimitrakakis and published by Springer Nature. This book was released on 2022-12-02 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 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.
Book Synopsis Decision Making Under Uncertainty by : Mykel J. Kochenderfer
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.
Book Synopsis Algorithms for Decision Making by : Mykel J. Kochenderfer
Download or read book Algorithms for Decision Making written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2022-08-16 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.
Book Synopsis Decision Making Under Uncertainty and Constraints by : Martine Ceberio
Download or read book Decision Making Under Uncertainty and Constraints written by Martine Ceberio and published by Springer Nature. This book was released on 2023-01-03 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 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.
Book Synopsis Decision Making Under Uncertainty, with a Special Emphasis on Geosciences and Education by : Laxman Bokati
Download or read book Decision Making Under Uncertainty, with a Special Emphasis on Geosciences and Education written by Laxman Bokati and published by Springer Nature. This book was released on 2023-03-21 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 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.
Book Synopsis The Logic of Adaptive Behavior by : Martijn van Otterlo
Download or read book The Logic of Adaptive Behavior written by Martijn van Otterlo and published by . This book was released on 2009 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov decision processes have become the de facto standard in modeling and solving sequential decision making problems under uncertainty. This book studies lifting Markov decision processes, reinforcement learning and dynamic programming to the first-order (or, relational) setting.
Book Synopsis Decision Making: Neural and Behavioural Approaches by :
Download or read book Decision Making: Neural and Behavioural Approaches written by and published by Newnes. This book was released on 2013-01-10 with total page 533 pages. Available in PDF, EPUB and Kindle. Book excerpt: This well-established international series examines major areas of basic and clinical research within neuroscience, as well as emerging and promising subfields.This volume explores interdisciplinary research on decision making taking a neural and behavioural approach Leading authors review the state-of-the-art in their field of investigation, and provide their views and perspectives for future research Chapters are extensively referenced to provide readers with a comprehensive list of resources on the topics covered All chapters include comprehensive background information and are written in a clear form that is also accessible to the non-specialist
Book Synopsis Bounded Rationality in Decision Making Under Uncertainty: Towards Optimal Granularity by : Joe Lorkowski
Download or read book Bounded Rationality in Decision Making Under Uncertainty: Towards Optimal Granularity written by Joe Lorkowski and published by Springer. This book was released on 2017-07-01 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses an intriguing question: are our decisions rational? It explains seemingly irrational human decision-making behavior by taking into account our limited ability to process information. It also shows with several examples that optimization under granularity restriction leads to observed human decision-making. Drawing on the Nobel-prize-winning studies by Kahneman and Tversky, researchers have found many examples of seemingly irrational decisions: e.g., we overestimate the probability of rare events. Our explanation is that since human abilities to process information are limited, we operate not with the exact values of relevant quantities, but with “granules” that contain these values. We show that optimization under such granularity indeed leads to observed human behavior. In particular, for the first time, we explain the mysterious empirical dependence of betting odds on actual probabilities. This book can be recommended to all students interested in human decision-making, to researchers whose work involves human decisions, and to practitioners who design and employ systems involving human decision-making —so that they can better utilize our ability to make decisions under uncertainty.
Book Synopsis Decision Making Under Uncertainty by : David E. Bell
Download or read book Decision Making Under Uncertainty written by David E. Bell and published by Thomson South-Western. This book was released on 1995 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: These authors draw on nearly 50 years of combined teaching and consulting experience to give readers a straightforward yet systematic approach for making estimates about the likelihood and consequences of future events -- and then using those assessments to arrive at sound decisions. The book's real-world cases, supplemented with expository text and spreadsheets, help readers master such techniques as decision trees and simulation, such concepts as probability, the value of information, and strategic gaming; and such applications as inventory stocking problems, bidding situations, and negotiating.
Book Synopsis Reinforcement Learning and Stochastic Optimization by : Warren B. Powell
Download or read book Reinforcement Learning and Stochastic Optimization written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2022-03-15 with total page 1090 pages. Available in PDF, EPUB and Kindle. Book excerpt: REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.
Book Synopsis Decision Making: Uncertainty, Imperfection, Deliberation and Scalability by : Tatiana V. Guy
Download or read book Decision Making: Uncertainty, Imperfection, Deliberation and Scalability written by Tatiana V. Guy and published by Springer. This book was released on 2015-02-09 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selfish 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; • efficiently 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 fields.
Author :Tatiana Valentine Guy Publisher :Springer Science & Business Media ISBN 13 :3642246478 Total Pages :207 pages Book Rating :4.6/5 (422 download)
Book Synopsis Decision Making with Imperfect Decision Makers by : Tatiana Valentine Guy
Download or read book Decision Making with Imperfect Decision Makers written by Tatiana Valentine Guy and published by Springer Science & Business Media. This book was released on 2011-11-13 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported algorithmically. However, experimental data shows that real decision makers choose such Bayes-optimal decisions surprisingly infrequently, often making decisions that are badly sub-optimal. So prevalent is such imperfect decision-making that it should be accepted as an inherent feature of real decision makers living within interacting societies. To date such societies have been investigated from an economic and gametheoretic perspective, and even to a degree from a physics perspective. However, little research has been done from the perspective of computer science and associated disciplines like machine learning, information theory and neuroscience. This book is a major contribution to such research. Some of the particular topics addressed include: How should we formalise rational decision making of a single imperfect decision maker? Does the answer change for a system of imperfect decision makers? Can we extend existing prescriptive theories for perfect decision makers to make them useful for imperfect ones? How can we exploit the relation of these problems to the control under varying and uncertain resources constraints as well as to the problem of the computational decision making? What can we learn from natural, engineered, and social systems to help us address these issues?
Book Synopsis Decision Making under Uncertainty by : R.W. Scholz
Download or read book Decision Making under Uncertainty written by R.W. Scholz and published by Elsevier. This book was released on 1983-11-01 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the revised papers of an international symposium on research on fallacies, biases, and the development of decision behavior under uncertainty. The papers are organized in five main sections. The Introduction outlines the conceptual framework and how three of the sections - Cognitive Decision Research, Social Interaction, and Development and Epistemology - are interrelated and also how new fields, such as research into developmental questions, can be productively integrated. In the fifth section Comments are collected, which evaluate the impact of the contributions on decision research itself, and also on cognitive psychology, social psychology, economic theory, ant the discipline of mathematics education.
Book Synopsis Decision Making under Uncertainty by : Kerstin Preuschoff
Download or read book Decision Making under Uncertainty written by Kerstin Preuschoff and published by Frontiers Media SA. This book was released on 2015-06-16 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most decisions in life are based on incomplete information and have uncertain consequences. To successfully cope with real-life situations, the nervous system has to estimate, represent and eventually resolve uncertainty at various levels. A common tradeoff in such decisions involves those between the magnitude of the expected rewards and the uncertainty of obtaining the rewards. For instance, a decision maker may choose to forgo the high expected rewards of investing in the stock market and settle instead for the lower expected reward and much less uncertainty of a savings account. Little is known about how different forms of uncertainty, such as risk or ambiguity, are processed and learned about and how they are integrated with expected rewards and individual preferences throughout the decision making process. With this Research Topic we aim to provide a deeper and more detailed understanding of the processes behind decision making under uncertainty.
Book Synopsis Handbook of Reinforcement Learning and Control by : Kyriakos G. Vamvoudakis
Download or read book Handbook of Reinforcement Learning and Control written by Kyriakos G. Vamvoudakis and published by Springer Nature. This book was released on 2021-06-23 with total page 833 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.
Book Synopsis Machine Learning for Real-Time Decision Making by :
Download or read book Machine Learning for Real-Time Decision Making written by and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many problems of interest to the Air Force involve routine sequential decision making under uncertainty. Examples include air traffic control, control of autonomous surveillance aircraft, logistics planning and scheduling, and equipment diagnosis and repair. These kinds of problems can be formulated within the framework of Markov Decision Problems (MDPs) and Partially-Observable Markov Decision Problems (POMDPs). Reinforcement Learning is the study of adaptive methods for solving large MDPs and POMDPs. The research funded under this grant developed a hierarchical approach to solving MDPs, called the MAXQ method, that is much more effective than previous non-hierarchical methods. Theoretical analysis proves that MAXQ converges to the optimal solution. Experimental studies show that it gives very large speedups during learning. A second line of research developed two methods for approximately solving large POMDPs. This research also explored cost-sensitive learning and diagnosis by formulating them as POMDPs and applying specialized reinforcement learning methods to solve them. A third line of research focused on function approximation methods and algorithms for practical reinforcement learning. New representations (based on regression trees and support vector machines) and new algorithms (based on more appropriate objective functions) led to improvements in the quality of solutions and the practical application of reinforcement learning to resource-constrained scheduling problems.
Book Synopsis A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning by : Alborz Geramifard
Download or read book A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning written by Alborz Geramifard and published by . This book was released on 2013-12 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: This tutorial reviews techniques for planning and learning in Markov Decision Processes (MDPs) with linear function approximation of the value function. Two major paradigms for finding optimal policies were considered: dynamic programming (DP) techniques for planning and reinforcement learning (RL).
