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Models For Planning Under Uncertainty
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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 Models for Planning Under Uncertainty by : Peter L. Hammer
Download or read book Models for Planning Under Uncertainty written by Peter L. Hammer and published by . This book was released on 1995 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Models for Planning Under Uncertainty by : Hercules Vladimirou
Download or read book Models for Planning Under Uncertainty written by Hercules Vladimirou and published by . This book was released on 1995 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Artificial Intelligence Today by : Michael J. Wooldridge
Download or read book Artificial Intelligence Today written by Michael J. Wooldridge and published by Springer Science & Business Media. This book was released on 1999-08-18 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author :National Academies of Sciences, Engineering, and Medicine Publisher :National Academies Press ISBN 13 :0309450780 Total Pages :165 pages Book Rating :4.3/5 (94 download)
Book Synopsis Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions by : National Academies of Sciences, Engineering, and Medicine
Download or read book Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2017-03-06 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Office of the Under Secretary of Defense (Personnel & Readiness), referred to throughout this report as P&R, is responsible for the total force management of all Department of Defense (DoD) components including the recruitment, readiness, and retention of personnel. Its work and policies are supported by a number of organizations both within DoD, including the Defense Manpower Data Center (DMDC), and externally, including the federally funded research and development centers (FFRDCs) that work for DoD. P&R must be able to answer questions for the Secretary of Defense such as how to recruit people with an aptitude for and interest in various specialties and along particular career tracks and how to assess on an ongoing basis service members' career satisfaction and their ability to meet new challenges. P&R must also address larger-scale questions, such as how the current realignment of forces to the Asia-Pacific area and other regions will affect recruitment, readiness, and retention. While DoD makes use of large-scale data and mathematical analysis in intelligence, surveillance, reconnaissance, and elsewhereâ€"exploiting techniques such as complex network analysis, machine learning, streaming social media analysis, and anomaly detectionâ€"these skills and capabilities have not been applied as well to the personnel and readiness enterprise. Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions offers and roadmap and implementation plan for the integration of data analysis in support of decisions within the purview of P&R.
Book Synopsis Planning Under Uncertainty by : Gerd Infanger
Download or read book Planning Under Uncertainty written by Gerd Infanger and published by Boyd & Fraser Publishing Company. This book was released on 1994 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Stochastic Model of Actions and Plans for Anytime Planning Under Uncertainty by : International Computer Science Institute
Download or read book A Stochastic Model of Actions and Plans for Anytime Planning Under Uncertainty written by International Computer Science Institute and published by . This book was released on 1993 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Building planning systems that operate in real domains requires coping with both uncertainty and time pressure. This paper describes a model of reaction plans, which are generated using a formalization of actions and of state descriptions in probabilistic logic, as a basis for anytime planning under uncertainty. The model has the following main features. At the action level, we handle incomplete and ambiguous domain information, and reason about alternative action effects whose probabilities are given. On this basis, we generate reaction plans that specify different courses of action, reflecting the domain uncertainty and alternative action effects; if generation time was insufficient, these plans may be left unfinished, but they can be reused, incrementally improved, and finished later. At the planning level, we develop a framework for measuring the quality of plans that takes domain uncertainty and probabilistic information into account using Markov chain theory; based on this framework, one can design anytime algorithms focusing on those parts of an unfinished plan first, whose completion promises the most 'gain.' Finally, the plan quality can be updated during execution, according to additional information acquired, and can therefore be used for on-line planning."
Book Synopsis Defense Resource Planning Under Uncertainty by : Robert J. Lempert
Download or read book Defense Resource Planning Under Uncertainty written by Robert J. Lempert and published by Rand Corporation. This book was released on 2016-01-29 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Modeling Uncertainty by : Moshe Dror
Download or read book Modeling Uncertainty written by Moshe Dror and published by Springer Science & Business Media. This book was released on 2002-01-31 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: Writing in honour of Sid Yakowitz, 50 internationally known scholars have collectively contributed 30 papers on modelling uncertainty to this volume. These include papers with a theoretical emphasis and others that focus on applications.
Book Synopsis Models of Scenario Building and Planning by : A. Martelli
Download or read book Models of Scenario Building and Planning written by A. Martelli and published by Palgrave Macmillan. This book was released on 2016-08-27 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Models of Scenario Building and Planning offers a unique and innovative exploration of the scenario approach. The book focuses on the analysis of the competitors' behavior; the analysis of risk and uncertainty; and the link between scenarios and strategies.
Book Synopsis Planning Under Uncertainty with Bayesian Nonparametric Models by : Robert Henry Klein
Download or read book Planning Under Uncertainty with Bayesian Nonparametric Models written by Robert Henry Klein and published by . This book was released on 2014 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous agents are increasingly being called upon to perform challenging tasks in complex settings with little information about underlying environment dynamics. To successfully complete such tasks the agent must learn from its interactions with the environment. Many existing techniques make assumptions about problem structure to remain tractable, such as limiting the class of possible models or specifying a fixed model expressive power. Complicating matters, there are many scenarios where the environment exhibits multiple underlying sets of dynamics; in these cases, most existing approaches assume the number of underlying models is known a priori, or ignore the possibility of multiple models altogether. Bayesian nonparametric (BNP) methods provide the flexibility to solve both of these problems, but have high inference complexity that has limited their adoption. This thesis provides several methods to tractably plan under uncertainty using BNPs. The first is Simultaneous Clustering on Representation Expansion (SCORE) for learning Markov Decision Processes (MDPs) that exhibit an underlying multiple-model structure. SCORE addresses the co-dependence between observation clustering and model expansion. The second contribution provides a realtime, non-myopic, risk-aware planning solution for use in camera surveillance scenarios where the number of underlying target behaviors and their parameterization are unknown. A BNP model is used to capture target behaviors, and a solution that reduces uncertainty only as needed to perform a mission is presented for allocating cameras. The final contribution is a reinforcement learning (RL) framework RLPy, a software package to promote collaboration and speed innovation in the RL community. RLPy provides a library of learning agents, function approximators, and problem domains for performing RL experiments. RLPy also provides a suite of tools that help automate tasks throughout the experiment pipeline, from initial prototyping through hyperparameter optimization, parallelization of large-scale experiments, and final publication-ready plotting.
Book Synopsis Decision Making under Deep Uncertainty by : Vincent A. W. J. Marchau
Download or read book Decision Making under Deep Uncertainty written by Vincent A. W. J. Marchau and published by Springer. This book was released on 2019-04-04 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Decision Making Under Uncertainty in Electricity Markets by : Antonio J. Conejo
Download or read book Decision Making Under Uncertainty in Electricity Markets written by Antonio J. Conejo and published by Springer Science & Business Media. This book was released on 2010-09-08 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Interactive Planning Under Uncertainty with Casual Modeling and Analysis by :
Download or read book Interactive Planning Under Uncertainty with Casual Modeling and Analysis written by and published by . This book was released on 2006 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper describes a new technique for interactive planning under conditions of uncertainty. Our approach is based on the use of the Air Force Research Laboratory's Causal Analysis Tool (CAT), a system for creating and analyzing causal models similar to Bayes networks. In order to use CAT as a tool for planning, users go through an iterative process in which they use CAT to create and analyze alternative plans. One of the biggest difficulties is that the number of possible plans is exponential. In any planning problem of significant size, it is impossible for the user to create and analyze every possible plan; thus users can spend days arguing about which actions to include in their plans. To solve this problem, we have developed a way to quickly compute the minimum and maximum probabilities of success associated with a partial plan, and use these probabilities to recommend which actions the user should include in the plan in order to get the plan that has the highest probability of success. This provides an exponential reduction in amount of time needed to find the best plan.
Book Synopsis Scalable Methods and Expressive Models for Planning Under Uncertainty by : Andrey Kolobov
Download or read book Scalable Methods and Expressive Models for Planning Under Uncertainty written by Andrey Kolobov and published by . This book was released on 2013 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ability to plan in the presence of uncertainty about the effects of one's own actions and the events of the environment is a core skill of a truly intelligent agent. This type of sequential decision-making has been modeled by Markov Decision Processes (MDPs), a framework known since at least the 1950's. The importance of MDPs is not merely philosophic --- they have been applied to several impactful real-world scenarios, from inventory management to military operations planning. Nonetheless, the adoption of MDPs in practice is greatly hampered by two aspects. First, modern algorithms for solving them are still not scalable enough to handle many realistically-sized problems. Second, the MDP classes we know how to solve tend to be restrictive, often failing to model significant aspects of the planning task at hand. As a result, many probabilistic scenarios fall outside of MDPs' scope. The research presented in this dissertation addresses both of these challenges. Its first contribution is several highly scalable approximation algorithms for existing MDP classes that combine two major planning paradigms, dimensionality reduction and deterministic relaxation. These approaches automatically extract human-understandable causal structure from an MDP and use this structure to efficiently compute a good MDP policy. Besides enabling us to handle larger planning scenarios, they bring us closer to the ideal of AI --- building agents that autonomously recognize features important for solving a problem. While these techniques are applicable only to goal-oriented scenarios, this dissertation also introduces approximation algorithms for reward-oriented settings. The second contribution of this work is new MDP classes that take into account previously ignored aspects of planning scenarios, e.g., the possibility of catastrophic failures. The thesis explores their mathematical properties and proposes algorithms for solving these problems.
Book Synopsis Optimal Decisions Under Uncertainty by : J.K. Sengupta
Download or read book Optimal Decisions Under Uncertainty written by J.K. Sengupta and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding the stochastic enviornment is as much important to the manager as to the economist. From production and marketing to financial management, a manager has to assess various costs imposed by uncertainty. The economist analyzes the role of incomplete and too often imperfect information structures on the optimal decisions made by a firm. The need for understanding the role of uncertainty in quantitative decision models, both in economics and management science provide the basic motivation of this monograph. The stochastic environment is analyzed here in terms of the following specific models of optimization: linear and quadratic models, linear programming, control theory and dynamic programming. Uncertainty is introduced here through the para meters, the constraints, and the objective function and its impact evaluated. Specifically recent developments in applied research are emphasized, so that they can help the decision-maker arrive at a solution which has some desirable charac teristics like robustness, stability and cautiousness. Mathematical treatment is kept at a fairly elementary level and applied as pects are emphasized much more than theory. Moreover, an attempt is made to in corporate the economic theory of uncertainty into the stochastic theory of opera tions research. Methods of optimal decision rules illustrated he re are applicable in three broad areas: (a) applied economic models in resource allocation and economic planning, (b) operations research models involving portfolio analysis and stochastic linear programming and (c) systems science models in stochastic control and adaptive behavior.
Book Synopsis Optimization of Integrated Supply Chain Planning under Multiple Uncertainty by : Juping Shao
Download or read book Optimization of Integrated Supply Chain Planning under Multiple Uncertainty written by Juping Shao and published by Springer. This book was released on 2015-05-27 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
