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Book Ambiguous Partially Observable Markov Decision Processes

Download or read book Ambiguous Partially Observable Markov Decision Processes written by Soroush Saghafian and published by . This book was released on 2018 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Decision Processes (MDPs) have been widely used as invaluable tools in dynamic decision-making, which is a central concern for economic agents operating at both the micro and macro levels. Often the decision maker's information about the state is incomplete; hence, the generalization to Partially Observable MDPs (POMDPs). Unfortunately, POMDPs may require a large state and/or action space, creating the well-known "curse of dimensionality." However, recent computational contributions and blindingly fast computers have helped to dispel this curse. This paper introduces and addresses a second curse termed "curse of ambiguity," which refers to the fact that the exact transition probabilities are often hard to quantify, and are rather ambiguous. For instance, for a monetary authority concerned with dynamically setting the inflation rate so as to control the unemployment, the dynamics of unemployment rate under any given inflation rate is often ambiguous. Similarly, in worker-job matching, the dynamics of worker-job match/proficiency level is typically ambiguous. This paper addresses the "curse of ambiguity" by developing a generalization of POMDPs termed Ambiguous POMDPs (APOMDPs), which not only allows the decision maker to take into account imperfect state information, but also tackles the inevitable ambiguity with respect to the correct probabilistic model of transitions. Importantly, this paper extends various structural results from POMDPs to APOMDPs. These results enable the decision maker to make robust decisions. Robustness is achieved by using a-maximin expected utility (a-MEU), which (a) differentiates between ambiguity and ambiguity attitude, (b) avoids the over conservativeness of traditional maximin approaches, and (c) is found to be suitable in laboratory experiments in various choice behaviors including those in portfolio selection. The structural results provided also help to handle the "curse of dimensionality," since they significantly simplify the search for an optimal policy. The analysis identifies a performance guarantee for the proposed approach by developing a bound for its maximum reward loss due to model ambiguity.

Book Data Driven Management of Post Transplant Medications

Download or read book Data Driven Management of Post Transplant Medications written by Alireza Boloori and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Organ-transplanted patients typically receive high amounts of immunosuppressive drugs (e.g., tacrolimus) as a mechanism to reduce their risk of organ rejection. However, due to the diabetogenic effect of these drugs, this practice exposes them to greater risk of New-Onset Diabetes After Transplant (NODAT), and hence, becoming insulin-dependent. This common conundrum of balancing the risk of organ rejection versus that of NODAT is further complicated due to various factors that create ambiguity in quantifying risks: (1) false-positive and false-negative errors of medical tests, (2) inevitable estimation errors when data sets are used, (3) variability among physicians' attitudes towards ambiguous outcomes, and (4) dynamic and patient risk-profile dependent progression of health conditions. To address these challenges, we use an ambiguous partially observable Markov decision process (APOMDP) framework, where dynamic optimization with respect to a “cloud” of possible models allows us to make decisions that are robust to misspecifications of risks. We first provide various structural results that facilitate characterizing the optimal policy. Using a clinical data set, we then compare the optimal policy to the current practice as well as some other benchmarks, and discuss various implications for both policy makers and physicians. In particular, our results show that substantial improvements are achievable in two important dimensions: (a) the quality-adjusted life expectancy (QALE) of patients, and (b) medical expenditures.

Book Robust Partially Observable Markov Decision Processes

Download or read book Robust Partially Observable Markov Decision Processes written by Mohammad Rasouli and published by . This book was released on 2018 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a variety of applications, decisions needs to be made dynamically after receiving imperfect observations about the state of an underlying system. Partially Observable Markov Decision Processes (POMDPs) are widely used in such applications. To use a POMDP, however, a decision-maker must have access to reliable estimations of core state and observation transition probabilities under each possible state and action pair. This is often challenging mainly due to lack of ample data, especially when some actions are not taken frequently enough in practice. This significantly limits the application of POMDPs in real-world settings. In healthcare, for example, medical tests are typically subject to false-positive and false-negative errors, and hence, the decision-maker has imperfect information about the health state of a patient. Furthermore, since some treatment options have not been recommended or explored in the past, data cannot be used to reliably estimate all the required transition probabilities regarding the health state of the patient. We introduce an extension of POMDPs, termed Robust POMDPs (RPOMDPs), which allows dynamic decision-making when there is ambiguity regarding transition probabilities. This extension enables making robust decisions by reducing the reliance on a single probabilistic model of transitions, while still allowing for imperfect state observations. We develop dynamic programming equations for solving RPOMDPs, provide a sufficient statistic and an information state, discuss ways in which their computational complexity can be reduced, and connect them to stochastic zero-sum games with imperfect private monitoring.

Book Markov Decision Processes with Applications to Finance

Download or read book Markov Decision Processes with Applications to Finance written by Nicole Bäuerle and published by Springer Science & Business Media. This book was released on 2011-06-06 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theory of Markov decision processes focuses on controlled Markov chains in discrete time. The authors establish the theory for general state and action spaces and at the same time show its application by means of numerous examples, mostly taken from the fields of finance and operations research. By using a structural approach many technicalities (concerning measure theory) are avoided. They cover problems with finite and infinite horizons, as well as partially observable Markov decision processes, piecewise deterministic Markov decision processes and stopping problems. The book presents Markov decision processes in action and includes various state-of-the-art applications with a particular view towards finance. It is useful for upper-level undergraduates, Master's students and researchers in both applied probability and finance, and provides exercises (without solutions).

Book Partially Observed Markov Decision Processes

Download or read book Partially Observed Markov Decision Processes written by Vikram Krishnamurthy and published by Cambridge University Press. This book was released on 2016-03-21 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers formulation, algorithms, and structural results of partially observed Markov decision processes, whilst linking theory to real-world applications in controlled sensing. Computations are kept to a minimum, enabling students and researchers in engineering, operations research, and economics to understand the methods and determine the structure of their optimal solution.

Book PARTIALLY OBSERVABLE MARKOV PROCESSES

Download or read book PARTIALLY OBSERVABLE MARKOV PROCESSES written by J. David R Kramer (Jr) and published by . This book was released on 1964 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: A partially observable Markov process is a model of a discrete time dynamic system which takes into account the effects of imperfect observations and of random system be havior. The model consists of an underlying Markov process with state vector X(n). Direct observations of X(n) are not possible, but a vector Z(n) is observed. The observation Z(n) is related to the state X(n) by a known probability density function. This model is useful in the analysis of a very large class of sequential decision problems. It was shown that a partially observable Markov process is conveniently analyzed by the introduction of the probability density function. This density function was shown to have certain characteristic iterative properties and is referred to as the statistical state of the system. The application of the theory of partially observable Markov processes to the problems of estimation, prediction and smoothing is straightforward. When a general terminal control problem is considered, however, the notion of minimum expected cost turns out to be ambiguous. The concepts of a priori and a posteriori control were introduced to reslove this confusion. (Author).

Book Exploiting Structure to Efficiently Solve Large Scale Partially Observable Markov Decision Processes microform

Download or read book Exploiting Structure to Efficiently Solve Large Scale Partially Observable Markov Decision Processes microform written by Pascal Poupart and published by Library and Archives Canada = Bibliothèque et Archives Canada. This book was released on 2005 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Partially observable Markov decision processes (POMDPs) provide a natural and principled framework to model a wide range of sequential decision making problems under uncertainty. To date, the use of POMDPs in real-world problems has been limited by the poor scalability of existing solution algorithms, which can only solve problems with up to ten thousand states. In fact, the complexity of finding an optimal policy for a finite-horizon discrete POMDP is PSPACE-complete. In practice, two important sources of intractability plague most solution algorithms: Large policy spaces and large state spaces. In practice, it is critical to simultaneously mitigate the impact of complex policy representations and large state spaces. Hence, this thesis describes three approaches that combine techniques capable of dealing with each source of intractability: VDC with BPI, VDC with Perseus (a randomized point-based value iteration algorithm by Spaan and Vlassis [136]), and state abstraction with Perseus. The scalability of those approaches is demonstrated on two problems with more than 33 million states: synthetic network management and a real-world system designed to assist elderly persons with cognitive deficiencies to carry out simple daily tasks such as hand-washing. This represents an important step towards the deployment of POMDP techniques in ever larger, real-world, sequential decision making problems. On the other hand, for many real-world POMDPs it is possible to define effective policies with simple rules of thumb. This suggests that we may be able to find small policies that are near optimal. This thesis first presents a Bounded Policy Iteration (BPI) algorithm to robustly find a good policy represented by a small finite state controller. Real-world POMDPs also tend to exhibit structural properties that can be exploited to mitigate the effect of large state spaces. To that effect, a value-directed compression (VDC) technique is also presented to reduce POMDP models to lower dimensional representations.

Book Partially Observable Markov Decision Process

Download or read book Partially Observable Markov Decision Process written by Gerard Blokdyk and published by Createspace Independent Publishing Platform. This book was released on 2018-05-29 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Which customers cant participate in our Partially observable Markov decision process domain because they lack skills, wealth, or convenient access to existing solutions? Can we add value to the current Partially observable Markov decision process decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)? Who are the people involved in developing and implementing Partially observable Markov decision process? How does Partially observable Markov decision process integrate with other business initiatives? Does the Partially observable Markov decision process performance meet the customer's requirements? This premium Partially observable Markov decision process self-assessment will make you the assured Partially observable Markov decision process domain master by revealing just what you need to know to be fluent and ready for any Partially observable Markov decision process challenge. How do I reduce the effort in the Partially observable Markov decision process work to be done to get problems solved? How can I ensure that plans of action include every Partially observable Markov decision process task and that every Partially observable Markov decision process outcome is in place? How will I save time investigating strategic and tactical options and ensuring Partially observable Markov decision process costs are low? How can I deliver tailored Partially observable Markov decision process advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Partially observable Markov decision process essentials are covered, from every angle: the Partially observable Markov decision process self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Partially observable Markov decision process outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Partially observable Markov decision process practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Partially observable Markov decision process are maximized with professional results. Your purchase includes access details to the Partially observable Markov decision process self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book.

Book Algorithms for Partially Observable Markov Decision Processes

Download or read book Algorithms for Partially Observable Markov Decision Processes written by Hsien-Te Cheng and published by . This book was released on 1988 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Learning in Partially Observable Markov Decision Processes

Download or read book Learning in Partially Observable Markov Decision Processes written by Mohit Sachan and published by . This book was released on 2012 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning in Partially Observable Markov Decision process (POMDP) is motivated by the essential need to address a number of realistic problems. A number of methods exist for learning in POMDPs, but learning with limited amount of information about the model of POMDP remains a highly anticipated feature. Learning with minimal information is desirable in complex systems as methods requiring complete information among decision makers are impractical in complex systems due to increase of problem dimensionality. In this thesis we address the problem of decentralized control of POMDPs with unknown transition probabilities and reward. We suggest learning in POMDP using a tree based approach. States of the POMDP are guessed using this tree. Each node in the tree has an automaton in it and acts as a decentralized decision maker for the POMDP. The start state of POMDP is known as the landmark state. Each automaton in the tree uses a simple learning scheme to update its action choice and requires minimal information. The principal result derived is that, without proper knowledge of transition probabilities and rewards, the automata tree of decision makers will converge to a set of actions that maximizes the long term expected reward per unit time obtained by the system. The analysis is based on learning in sequential stochastic games and properties of ergodic Markov chains. Simulation results are presented to compare the long term rewards of the system under different decision control algorithms.

Book Partially Observable Markov Decision Processes with Applications

Download or read book Partially Observable Markov Decision Processes with Applications written by Dale J. Hockstra and published by . This book was released on 1973 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study examines a class of partially observable sequential decision models motivated by the process of machine maintenance and corrective action or medical diagnosis and treatment. Emphasis is placed on the dynamics of the state, i.e., the possibility that the machine (disease) state changes during the decision process. This is incorporated in the form of a Markov chain. It is also assumed that the state is only indirectly observable via outputs probabilistically related to the state. The end result is a model which is a discrete time Markov decision process with a continuous state space, a finite action space, and a special transition structure. (Modified author abstract).

Book Algorithms for Partially Observable Markov Decision Processes

Download or read book Algorithms for Partially Observable Markov Decision Processes written by Weihong Zhang and published by . This book was released on 2001 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Parameter Estimation for Partially Observable Markov Decision Processes

Download or read book Parameter Estimation for Partially Observable Markov Decision Processes written by Srinivas S. Kambhamettu and published by . This book was released on 2000 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Algorithms for Partially Observable Markov Decision Processes

Download or read book Algorithms for Partially Observable Markov Decision Processes written by Hsien-Te Cheng and published by . This book was released on 1988 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: