Decision Making Under Uncertainty

Decision Making Under Uncertainty

Author: Mykel J. Kochenderfer

Publisher: MIT Press

Published: 2015-07-24

Total Pages: 350

ISBN-13: 0262331713

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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.


Artificial Intelligence Today

Artificial Intelligence Today

Author: Michael J. Wooldridge

Publisher: Springer Science & Business Media

Published: 1999-08-18

Total Pages: 489

ISBN-13: 3540664289

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Artificial Intelligence is one of the most fascinating and unusual areas of academic study to have emerged this century. For some, AI is a true scientific discipline, that has made important and fundamental contributions to the use of computation for our understanding of nature and phenomena of the human mind; for others, AI is the black art of computer science. Artificial Intelligence Today provides a showcase for the field of AI as it stands today. The editors invited contributions both from traditional subfields of AI, such as theorem proving, as well as from subfields that have emerged more recently, such as agents, AI and the Internet, or synthetic actors. The papers themselves are a mixture of more specialized research papers and authorative survey papers. The secondary purpose of this book is to celebrate Springer-Verlag's Lecture Notes in Artificial Intelligence series.


Decision Making Under Uncertainty in Electricity Markets

Decision Making Under Uncertainty in Electricity Markets

Author: Antonio J. Conejo

Publisher: Springer Science & Business Media

Published: 2010-09-08

Total Pages: 549

ISBN-13: 1441974210

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Decision Making Under Uncertainty in Electricity Markets provides models and procedures to be used by electricity market agents to make informed decisions under uncertainty. These procedures rely on well established stochastic programming models, which make them efficient and robust. Particularly, these techniques allow electricity producers to derive offering strategies for the pool and contracting decisions in the futures market. Retailers use these techniques to derive selling prices to clients and energy procurement strategies through the pool, the futures market and bilateral contracting. Using the proposed models, consumers can derive the best energy procurement strategies using the available trading floors. The market operator can use the techniques proposed in this book to clear simultaneously energy and reserve markets promoting efficiency and equity. The techniques described in this book are of interest for professionals working on energy markets, and for graduate students in power engineering, applied mathematics, applied economics, and operations research.


Defense Resource Planning Under Uncertainty

Defense Resource Planning Under Uncertainty

Author: Robert J. Lempert

Publisher: Rand Corporation

Published: 2016-01-29

Total Pages: 109

ISBN-13: 0833091670

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Defense planning faces significant uncertainties. This report applies robust decision making (RDM) to the munitions mix challenge, to demonstrate how RDM could help defense planners make plans more robust to a wide range of hard-to-predict futures.


Planning Algorithms

Planning Algorithms

Author: Steven M. LaValle

Publisher: Cambridge University Press

Published: 2006-05-29

Total Pages: 844

ISBN-13: 9780521862059

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Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications. Difficult mathematical material is explained through hundreds of examples and illustrations.


Decision Making under Deep Uncertainty

Decision Making under Deep Uncertainty

Author: Vincent A. W. J. Marchau

Publisher: Springer

Published: 2019-04-04

Total Pages: 408

ISBN-13: 3030052524

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This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them. Decision Making under Deep Uncertainty: From Theory to Practice is divided into four parts. Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis. Each approach is worked out in terms of its theoretical foundations, methodological steps to follow when using the approach, latest methodological insights, and challenges for improvement. In Part II, applications of each of these approaches are presented. Based on recent case studies, the practical implications of applying each approach are discussed in depth. Part III focuses on using the approaches and tools in real-world contexts, based on insights from real-world cases. Part IV contains conclusions and a synthesis of the lessons that can be drawn for designing, applying, and implementing strategic plans under deep uncertainty, as well as recommendations for future work. The publication of this book has been funded by the Radboud University, the RAND Corporation, Delft University of Technology, and Deltares.


Stochastic Programming: Applications In Finance, Energy, Planning And Logistics

Stochastic Programming: Applications In Finance, Energy, Planning And Logistics

Author: Horand I Gassmann

Publisher: World Scientific

Published: 2012-11-28

Total Pages: 549

ISBN-13: 9814407526

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This book shows the breadth and depth of stochastic programming applications. All the papers presented here involve optimization over the scenarios that represent possible future outcomes of the uncertainty problems. The applications, which were presented at the 12th International Conference on Stochastic Programming held in Halifax, Nova Scotia in August 2010, span the rich field of uses of these models. The finance papers discuss such diverse problems as longevity risk management of individual investors, personal financial planning, intertemporal surplus management, asset management with benchmarks, dynamic portfolio management, fixed income immunization and racetrack betting. The production and logistics papers discuss natural gas infrastructure design, farming Atlantic salmon, prevention of nuclear smuggling and sawmill planning. The energy papers involve electricity production planning, hydroelectric reservoir operations and power generation planning for liquid natural gas plants. Finally, two telecommunication papers discuss mobile network design and frequency assignment problems./a


Optimization of Integrated Supply Chain Planning under Multiple Uncertainty

Optimization of Integrated Supply Chain Planning under Multiple Uncertainty

Author: Juping Shao

Publisher: Springer

Published: 2015-05-27

Total Pages: 197

ISBN-13: 3662472503

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​The subject of this book is supply chain logistics planning optimization under multiple uncertainties, the key issue in supply chain management. Focusing on the strategic-alliance three-level supply chain, the model of supply chain logistics planning was established in terms of the market prices and the market requirements as random variables of manufactured goods with random expected value programming theory, and the hybrid intelligence algorithm solution model was designed. Aiming at the decentralized control supply chain, in which the nodes were unlimited expansion, the chance-constrained stochastic programming model was created in order to obtain optimal decision-making at a certain confidence level. In addition, the hybrid intelligence algorithm model was designed to solve the problem of supply chain logistics planning with the prices of the raw-materials supply market of the upstream enterprises and the prices of market demand for products of the downstream enterprises as random variables in the supply chain unit. Aimed at the three-stage mixed control supply chain, a logistics planning model was designed using fuzzy random programming theory with customer demand as fuzzy random variables and a hybrid intelligence algorithm solution was created. The research has significance both in theory and practice. Its theoretical significance is that the research can complement and perfect existing supply chain planning in terms of quantification. Its practical significance is that the results will guide companies in supply chain logistics planning in the uncertain environment.