Relevant bibliographies by topics / Approximate Decision

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Approximate Decision'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Approximate Decision"

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Schirra, Stefan. "Approximate decision algorithms for approximate congruence." Information Processing Letters 43, no. 1 (August 1992): 29–34. http://dx.doi.org/10.1016/0020-0190(92)90025-q.

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Syau, Yu-Ru, En-Bing Lin, and Lixing Jia. "Discernibility Thresholds and Approximate Dependency in Analysis of Decision Tables." Journal of Computers 10, no. 6 (November 2015): 412–17. http://dx.doi.org/10.17706/jcp.10.6.412-417.

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Kuśmierczyk, Tomasz, Joseph Sakaya, and Arto Klami. "Correcting Predictions for Approximate Bayesian Inference." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4511–18. http://dx.doi.org/10.1609/aaai.v34i04.5879.

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Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to incorrect posterior predictive distributions. We present a novel approach that corrects for inaccuracies in posterior inference by altering the decision-making process. We train a separate model to make optimal decisions under the approximate posterior, combining interpretable Bayesian modeling with optimization of direct predictive accuracy in a principled fashion. The solution is generally applicable as a plug-in module for predictive decision-making for arbitrary probabilistic programs, irrespective of the posterior inference strategy. We demonstrate the approach empirically in several problems, confirming its potential.
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Dearden, Richard, and Craig Boutilier. "Abstraction and approximate decision-theoretic planning." Artificial Intelligence 89, no. 1-2 (January 1997): 219–83. http://dx.doi.org/10.1016/s0004-3702(96)00023-9.

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Ulieru, Mihaela. "Approximate Reasoning Approaches for Diagnostic Decision." IFAC Proceedings Volumes 27, no. 5 (June 1994): 339–44. http://dx.doi.org/10.1016/s1474-6670(17)48050-5.

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Heffernan, Paul J., and Stefan Schirra. "Approximate decision algorithms for point set congruence." Computational Geometry 4, no. 3 (July 1994): 137–56. http://dx.doi.org/10.1016/0925-7721(94)90004-3.

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Jayawardena, Chandimal, Keigo Watanabe, and Kiyotaka Izumi. "Learning from Approximate Human Decisions by a Robot." Journal of Robotics and Mechatronics 19, no. 1 (February 20, 2007): 68–76. http://dx.doi.org/10.20965/jrm.2007.p0068.

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Robot systems operating under natural-language commands must be able to infer the meaning intended by the issuer. Despite some successful research, however, an important related aspect not yet addressed has been the possibility of learning from natural-language commands. Such commands, generated by human users, contain valuable information. The inherent subjectivity of natural language, however, complicates potential learning from such commands and their interpretation. We propose decision making for robots operating under natural-language commands influenced by human aspects of decision making. Under our proposed concept, demonstrated in experiments conducted using a robotic manipulator, the robot is controlled using natural-language commands to conduct pick-and-place operations, during which the robot builds a knowledge base. After this learning, which uses a probabilistic neural network, the robot conducts similar tasks based on approximate decisions from the knowledge gained.
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Li, Mengmeng, Chiping Zhang, Minghao Chen, and Weihua Xu. "On local multigranulation covering decision-theoretic rough sets." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11107–30. http://dx.doi.org/10.3233/jifs-202274.

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Multi-granulation decision-theoretic rough sets uses the granular structures induced by multiple binary relations to approximate the target concept, which can get a more accurate description of the approximate space. However, Multi-granulation decision-theoretic rough sets is very time-consuming to calculate the approximate value of the target set. Local rough sets not only inherits the advantages of classical rough set in dealing with imprecise, fuzzy and uncertain data, but also breaks through the limitation that classical rough set needs a lot of labeled data. In this paper, in order to make full use of the advantage of computational efficiency of local rough sets and the ability of more accurate approximation space description of multi-granulation decision-theoretic rough sets, we propose to combine the local rough sets and the multigranulation decision-theoretic rough sets in the covering approximation space to obtain the local multigranulation covering decision-theoretic rough sets model. This provides an effective tool for discovering knowledge and making decisions in relation to large data sets. We first propose four types of local multigranulation covering decision-theoretic rough sets models in covering approximation space, where a target concept is approximated by employing the maximal or minimal descriptors of objects. Moreover, some important properties and decision rules are studied. Meanwhile, we explore the reduction among the four types of models. Furthermore, we discuss the relationships of the proposed models and other representative models. Finally, illustrative case of medical diagnosis is given to explain and evaluate the advantage of local multigranulation covering decision-theoretic rough sets model.
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Khaoula, Boutouhami, and Khellaf Faiza. "Optimistic Decision Making using an Approximate Graphical Model." International Journal of Artificial Intelligence & Applications 6, no. 2 (March 31, 2015): 1–20. http://dx.doi.org/10.5121/ijaia.2015.6201.

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Jin, Shangzhu, Jun Peng, Zuojin Li, and Qiang Shen. "Bidirectional approximate reasoning-based approach for decision support." Information Sciences 506 (January 2020): 99–112. http://dx.doi.org/10.1016/j.ins.2019.08.019.

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Dissertations / Theses on the topic "Approximate Decision"

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Xie, Chen. "DYNAMIC DECISION APPROXIMATE EMPIRICAL REWARD (DDAER) PROCESSES." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398991609.

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Pratikakis, Nikolaos. "Multistage decisions and risk in Markov decision processes towards effective approximate dynamic programming architectures /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/31654.

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Thesis (Ph.D)--Chemical Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Jay H. Lee; Committee Member: Martha Grover; Committee Member: Matthew J. Realff; Committee Member: Shabbir Ahmed; Committee Member: Stylianos Kavadias. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Bailey, David Thomas. "Development of an optimal spatial decision-making system using approximate reasoning." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16202/.

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There is a recognised need for the continued improvement of both the techniques and technology for spatial decision support in infrastructure site selection. Many authors have noted that current methodologies are inadequate for real-world site selection decisions carried out by heterogeneous groups of decision-makers under uncertainty. Nevertheless despite numerous limitations inherent in current spatial problem solving methods, spatial decision support systems have been proven to increase decision-maker effectiveness when used. However, due to the real or perceived difficulty of using these systems few applications are actually in use to support decision-makers in siting decisions. The most common difficulties encountered involve standardising criterion ratings, and communicating results. This research has focused on the use of Approximate Reasoning to improve the techniques and technology of spatial decision support, and make them easier to use and understand. The algorithm developed in this research (ARAISS) is based on the use of natural language to describe problem variables such as suitability, certainty, risk and consensus. The algorithm uses a method based on type II fuzzy sets to represent problem variables. ARAISS was subsequently incorporated into a new Spatial Decision Support System (InfraPlanner) and validated by use in a real-world site selection problem at Australia's Brisbane Airport. Results indicate that Approximate Reasoning is a promising method for spatial infrastructure planning decisions. Natural language inputs and outputs, combined with an easily understandable multiple decision-maker framework created an environment conducive to information sharing and consensus building among parties. Future research should focus on the use of Genetic Algorithms and other Artificial Intelligence techniques to broaden the scope of existing work.
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Yu, Huizhen Ph D. Massachusetts Institute of Technology. "Approximate solution methods for partially observable Markov and semi-Markov decision processes." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35299.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 165-169).
We consider approximation methods for discrete-time infinite-horizon partially observable Markov and semi-Markov decision processes (POMDP and POSMDP). One of the main contributions of this thesis is a lower cost approximation method for finite-space POMDPs with the average cost criterion, and its extensions to semi-Markov partially observable problems and constrained POMDP problems, as well as to problems with the undiscounted total cost criterion. Our method is an extension of several lower cost approximation schemes, proposed individually by various authors, for discounted POMDP problems. We introduce a unified framework for viewing all of these schemes together with some new ones. In particular, we establish that due to the special structure of hidden states in a POMDP, there is a class of approximating processes, which are either POMDPs or belief MDPs, that provide lower bounds to the optimal cost function of the original POMDP problem. Theoretically, POMDPs with the long-run average cost criterion are still not fully understood.
(cont.) The major difficulties relate to the structure of the optimal solutions, such as conditions for a constant optimal cost function, the existence of solutions to the optimality equations, and the existence of optimal policies that are stationary and deterministic. Thus, our lower bound result is useful not only in providing a computational method, but also in characterizing the optimal solution. We show that regardless of these theoretical difficulties, lower bounds of the optimal liminf average cost function can be computed efficiently by solving modified problems using multichain MDP algorithms, and the approximating cost functions can be also used to obtain suboptimal stationary control policies. We prove the asymptotic convergence of the lower bounds under certain assumptions. For semi-Markov problems and total cost problems, we show that the same method can be applied for computing lower bounds of the optimal cost function. For constrained average cost POMDPs, we show that lower bounds of the constrained optimal cost function can be computed by solving finite-dimensional LPs. We also consider reinforcement learning methods for POMDPs and MDPs. We propose an actor-critic type policy gradient algorithm that uses a structured policy known as a finite-state controller.
(cont.) We thus provide an alternative to the earlier actor-only algorithm GPOMDP. Our work also clarifies the relationship between the reinforcement learning methods for POMDPs and those for MDPs. For average cost MDPs, we provide a convergence and convergence rate analysis for a least squares temporal difference (TD) algorithm, called LSPE, and previously proposed for discounted problems. We use this algorithm in the critic portion of the policy gradient algorithm for POMDPs with finite-state controllers. Finally, we investigate the properties of the limsup and liminf average cost functions of various types of policies. We show various convexity and concavity properties of these costfunctions, and we give a new necessary condition for the optimal liminf average cost to be constant. Based on this condition, we prove the near-optimality of the class of finite-state controllers under the assumption of a constant optimal liminf average cost. This result provides a theoretical guarantee for the finite-state controller approach.
by Huizhen Yu.
Ph.D.
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Löhndorf, Nils, David Wozabal, and Stefan Minner. "Optimizing Trading Decisions for Hydro Storage Systems using Approximate Dual Dynamic Programming." INFORMS, 2013. http://dx.doi.org/10.1287/opre.2013.1182.

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We propose a new approach to optimize operations of hydro storage systems with multiple connected reservoirs whose operators participate in wholesale electricity markets. Our formulation integrates short-term intraday with long-term interday decisions. The intraday problem considers bidding decisions as well as storage operation during the day and is formulated as a stochastic program. The interday problem is modeled as a Markov decision process of managing storage operation over time, for which we propose integrating stochastic dual dynamic programming with approximate dynamic programming. We show that the approximate solution converges towards an upper bound of the optimal solution. To demonstrate the efficiency of the solution approach, we fit an econometric model to actual price and in inflow data and apply the approach to a case study of an existing hydro storage system. Our results indicate that the approach is tractable for a real-world application and that the gap between theoretical upper and a simulated lower bound decreases sufficiently fast. (authors' abstract)
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Astaraky, Davood. "A Simulation Based Approximate Dynamic Programming Approach to Multi-class, Multi-resource Surgical Scheduling." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23622.

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The thesis focuses on a model that seeks to address patient scheduling step of the surgical scheduling process to determine the number of surgeries to perform in a given day. Specifically, provided a master schedule that provides a cyclic breakdown of total OR availability into specific daily allocations to each surgical specialty, we look to provide a scheduling policy for all surgeries that minimizes a combination of the lead time between patient request and surgery date, overtime in the ORs and congestion in the wards. We cast the problem of generating optimal control strategies into the framework of Markov Decision Process (MDP). The Approximate Dynamic Programming (ADP) approach has been employed to solving the model which would otherwise be intractable due to the size of the state space. We assess performance of resulting policy and quality of the driven policy through simulation and we provide our policy insights and conclusions.
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Chen, Xiaoting. "Optimal Control of Non-Conventional Queueing Networks: A Simulation-Based Approximate Dynamic Programming Approach." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427799942.

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Lipsky, Ari Moshe. "Bayesian decision-theoretic trial design operating characteristics and ethics, an approximate method, and time-trend bias /." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1970030561&sid=4&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Sosnowski, Scott T. "Approximate Action Selection For Large, Coordinating, Multiagent Systems." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1459468867.

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Ramirez, Jose A. "Optimal and Simulation-Based Approximate Dynamic Programming Approaches for the Control of Re-Entrant Line Manufacturing Models." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282329260.

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Books on the topic "Approximate Decision"

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Board, Ontario Environmental Assessment. In the matter of an application by the Corporation of the Town of Cobourg for amendment of its Certificate of Approval to permit the continued use of the existing landfill site located in Haldimand Township, for a period of approximately four years: Reasons for decision and decision of the Board dated October 16, 1989. S.l: s.n, 1989.

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1944-, Sanchez Elie, and Zadeh Lotfi Asker, eds. Approximate reasoning in intelligent systems, decision and control. Oxford: Pergamon Press, 1987.

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Approximate Reasoning in Intelligent Systems, Decision and Control. Elsevier, 1987. http://dx.doi.org/10.1016/c2009-0-06816-0.

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Case-Based Approximate Reasoning (Theory and Decision Library B). Springer, 2007.

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Bouffard, Jeffrey A., and Nicole Niebuhr. Experimental Designs in the Study of Offender Decision Making. Edited by Wim Bernasco, Jean-Louis van Gelder, and Henk Elffers. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199338801.013.22.

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Research on offender decision making has utilized experimental designs and has often coupled these strong designs with the use of hypothetical vignettes that describe specific offending circumstances for the would-be offender to consider. In some cases, these studies have experimentally manipulated situational elements of the imagined setting. In others, researchers have experimentally manipulated the context in which the participants make the decision. Other researchers have utilized randomized designs with behavioral analogues within the research setting. This research has found that various situational and individual-level factors influence the content and process of offender decision making in important ways. Future research should further explore how offenders form risk perceptions and how these influences may interact with one another, and it should continue to refine these methods to more closely approximate real-world settings.
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Güth, Werner, and Hartmut Kliemt. Experimental Economics—A Philosophical Perspective. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199935314.013.16.

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The originally Hobbesian ideal of twentieth-century neoclassical economics as a discipline that studies human interaction “more geometrico” as a scenery of interactive rational decision making is rejected. “Explaining” overt behavior as (if it were) the equilibrium outcome of opportunity seeking rational choices is impossible if the requirement of approximate truth of the explanans is upheld. Stylized accounts of some central experiments (prisoner’s dilemma, ultimatum, dictator, impunity games, double oral auctions) show why this is so and illustrate basic contributions of experimental economics in an exemplary manner. A somewhat detailed account of an experiment concerning “equity” shows the explanatory potential and “workings” of experimental economics and how its findings can contribute to traditional philosophical and psychological discussions. Why the Humean “attempt to introduce the experimental method of reasoning into moral subjects” must remain incomplete until experimental economics and experimental psychology become fully complementary research strategies is indicated as well.
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Lee, Christoph I. Decision Rules for Imaging Acute Ankle Injuries. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190223700.003.0031.

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This chapter, found in the bone, joint, and extremity pain section of the book, provides a succinct synopsis of a key study examining the use of the Ottawa ankle rules for imaging acute ankle injuries. This summary outlines the study methodology and design, major results, limitations and criticisms, related studies and additional information, and clinical implications. The study showed that the refined and validated Ottawa Ankle Rules have the potential to reduce approximately 30% to 34% of all foot and ankle radiographs for acute injuries, with 100% sensitivity for reliably detecting foot and ankle fractures. In addition to outlining the most salient features of the study, a clinical vignette and imaging example are included in order to provide relevant clinical context.
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Beal, Jules C., and Emilio Perucca. Medical Management of Epilepsy. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0044.

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Epilepsy affects approximately 65 million people worldwide, leading to significant morbidity and mortality including injuries, psychiatric comorbidities, social stress, and the risk of sudden death. The best indicator of quality of life in patients with epilepsy is seizure freedom. The chapter discusses the medical management of epilepsy, focusing on decision making, when to initiate treatment, how to choose an appropriate medication, and how to proceed when a medication fails. The treatment of epilepsy is a highly individualized process that must take into account an individual’s seizure type, risk of seizure recurrence, age, sex, medical comorbidities, and personal goals and preferences.
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Shah, Ashish H., and Jacques J. Morcos. Dermoid/Epidermoid Tumors. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190696696.003.0018.

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Epidermoid tumors of the central nervous system are typically found in the cerebellopontine angle or parasellar space and comprise approximately 1% of all intracranial tumors. Dermoid cysts tend to occur in midline locations. Both are derived from embryonal tissue and have classic imaging findings on computed tomography and magnetic resonance imaging. Epidermoid tumors and dermoid cysts are benign and grow slowly, although epidermoid cysts can undergo malignant transformation. Surgical decisions and approaches are based on the presenting symptoms and anatomic location of the tumor. Mollaret meningitis is a unique complication of these cysts and may occur if the cyst ruptures preoperatively or during surgery.
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Selden, Nathan, and Lissa Baird, eds. Pediatric Neurosurgery. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190617073.001.0001.

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Part of the Neurosurgery by Example series, this volume on pediatric neurosurgery presents exemplary cases in which renowned authors guide readers through the assessment and planning, decision making, surgical procedure, aftercare, and complication management of common and uncommon disorders. As pediatric neurosurgery approximates the anatomical and pathophysiological breadth of all specialty areas of adult neurosurgery, the cases provided are particularly relevant to and more frequently encountered in pediatric practice. They also reflect aspects of clinical presentation and management that are notably distinct in pediatric compared to adult neurosurgery. Each chapter also contains “pivot points” that illuminate changes required to manage patients in alternate or atypical situations, and “pearls” for accurate diagnosis, successful treatment, and effective complication management. Containing a focused review of medical evidence and expected outcomes, Pediatric Neurosurgery is appropriate for neurosurgeons who wish to learn more about this subspecialty and those preparing for the American Board of Neurological Surgery oral examination.
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Book chapters on the topic "Approximate Decision"

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Dompere, Kofi Kissi. "Fuzzy Decision-Choice Rationality and Paradoxes in Decision-Choice Theories." In Fuzziness and Approximate Reasoning, 133–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88087-5_6.

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Meisel, Stephan. "Approximate Anticipation." In Anticipatory Optimization for Dynamic Decision Making, 63–75. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0505-4_5.

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Zeng, Dao-Zhi. "Approximate Envy-Free Procedures." In Theory and Decision Library, 259–71. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4627-6_17.

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Munos, Rémi. "Approximate Dynamic Programming." In Markov Decision Processes in Artificial Intelligence, 67–98. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557426.ch3.

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Dompere, Kofi Kissi. "Epistemics of Risk and Optimal Decision-Choice Rationality." In Fuzziness and Approximate Reasoning, 83–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88087-5_4.

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Dompere, Kofi Kissi. "Fuzzy Rationality, Ambiguity and Risk in Decision-Choice Process." In Fuzziness and Approximate Reasoning, 51–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88087-5_3.

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Dompere, Kofi Kissi. "Reflections on Some Decision-Choice Theories on Uncertainty and Risk." In Fuzziness and Approximate Reasoning, 105–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88087-5_5.

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Ślęzak, Dominik, and Agnieszka Chądzyńska-Krasowska. "Approximate Decision Tree Induction over Approximately Engineered Data Features." In Rough Sets, 376–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52705-1_28.

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Bouchon-Meunier, Bernadette, and Christophe Marsala. "Learning Fuzzy Decision Rules." In Fuzzy Sets in Approximate Reasoning and Information Systems, 279–304. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5243-7_5.

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Moshkov, Mikhail, and Beata Zielosko. "Approximate Tests, Decision Trees and Rules." In Combinatorial Machine Learning, 87–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20995-6_6.

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Conference papers on the topic "Approximate Decision"

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Julius, A. Agung, and George J. Pappas. "Approximate equivalence and approximate synchronization of metric transition systems." In Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.377763.

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Andrews, Burton W., Pablo A. Iglesias, and Eduardo D. Sontag. "Signal Detection and Approximate Adaptation Implies an Approximate Internal Model." In Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.377227.

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Wang, Xia, and Wei-Zhi Wu. "Approximate reduction in inconsistent formal decision contexts." In 2012 IEEE International Conference on Granular Computing (GrC-2012). IEEE, 2012. http://dx.doi.org/10.1109/grc.2012.6469272.

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Heffernan, Paul J., and Stefan Schirra. "Approximate decision algorithms for point set congruence." In the eighth annual symposium. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/142675.142697.

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Qin, Chunbin, Yizhe Huang, Yabin Yang, Jishi Zhang, and Xianxing Liu. "Approximate Optimal tracking Control for Nonlinear Discrete-time Switched Systems via Approximate Dynamic Programming." In 2019 Chinese Control And Decision Conference (CCDC). IEEE, 2019. http://dx.doi.org/10.1109/ccdc.2019.8832761.

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Mou-Yan, Zou, and R. Unbehauen. "Approximate factorization of bivariate polynomials." In 1986 25th IEEE Conference on Decision and Control. IEEE, 1986. http://dx.doi.org/10.1109/cdc.1986.267444.

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Tharakunnel, Kurian, and Siddhartha Bhattacharyya. "Leader-Follower semi-Markov Decision Problems: Theoretical Framework and Approximate Solution." In 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning. IEEE, 2007. http://dx.doi.org/10.1109/adprl.2007.368177.

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Wiering, Marco A., and Edwin D. de Jong. "Computing Optimal Stationary Policies for Multi-Objective Markov Decision Processes." In 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning. IEEE, 2007. http://dx.doi.org/10.1109/adprl.2007.368183.

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Barty, Kengy, Pierre Girardeau, Jean-Sebastien Roy, and Cyrille Strugarek. "Q-Learning with Continuous State Spaces and Finite Decision Set." In 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning. IEEE, 2007. http://dx.doi.org/10.1109/adprl.2007.368209.

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Salgado, M. E., B. Ninness, and G. C. Goodwin. "Approximate identification of linear stochastic systems." In 29th IEEE Conference on Decision and Control. IEEE, 1990. http://dx.doi.org/10.1109/cdc.1990.203371.

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Reports on the topic "Approximate Decision"

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Chang, Hyeong S., and Steven I. Marcus. Approximate Receding Horizon Approach for Markov Decision Processes: Average Award Case. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada438476.

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Garton, Timothy. Data enrichment and enhanced accessibility of waterborne commerce numerical data : spatially depicting the National Waterway Network. Engineer Research and Development Center (U.S.), December 2020. http://dx.doi.org/10.21079/11681/39223.

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This report provides methodologies and processes of data enrichment and enhanced accessibility of Waterborne Commerce and Statistics Center (WCSC) maintained databases. These databases house tabular and statistical data that reports on The U.S. Army Corps of Engineers (USACE) Civil Works Division National Waterway Network (NWN), which geospatially represents approximately 1,000 harbors and 25,000 miles of channels and waterways. WCSC is a division of The Institute for Water Resources (IWR). They have been tasked with the international collection, maintenance, and archival of all records involving commercial movements and commerce that occur on federal waterways. The current records structure is a large, tabular dataset and limited to the systems and processes put in place prior to the computing standards and capabilities available today. Methods have been tested and utilized to bring the tabular datasets into an optimized, modern geospatial network and expanded upon to create a higher resolution than previously maintained by the WCSC. This report will expand upon the applied methodologies to optimize data queries and the overall enhancement of the data system to allow for linkages to various other sources of information for commerce data enhancement for decision support assistance.
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3

Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, April 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington Metropolitan Area Transit Authority (WMATA) bus routes operating in Washington, DC. We developed regression models and Artificial Neural Network (ANN) models for predicting travel times of buses for different peak periods (AM, Mid-Day and PM). Our analysis included variables such as number of served bus stops, length of route between bus stops, average number of passengers in the bus, average dwell time of buses, and number of intersections between bus stops. We obtained ANN models for travel times by using approximation technique incorporating two separate algorithms: Quasi-Newton and Levenberg-Marquardt. The training strategy for neural network models involved feed forward and errorback processes that minimized the generated errors. We also evaluated the models with a Comparison of the Normalized Squared Errors (NSE). From the results, we observed that the travel times of buses and the dwell times at bus stops generally increased over time of the day. We gathered travel time equations for buses for the AM, Mid-Day and PM Peaks. The lowest NSE for the AM, Mid-Day and PM Peak periods corresponded to training processes using Quasi-Newton algorithm, which had 3, 2 and 5 perceptron layers, respectively. These prediction models could be adapted by transit agencies to provide the patrons with accurate travel time information at bus stops or online.
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Wright, Kirsten. Collecting Plant Phenology Data In Imperiled Oregon White Oak Ecosystems: Analysis and Recommendations for Metro. Portland State University, March 2020. http://dx.doi.org/10.15760/mem.64.

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Highly imperiled Oregon white oak ecosystems are a regional conservation priority of numerous organizations, including Oregon Metro, a regional government serving over one million people in the Portland area. Previously dominant systems in the Pacific Northwest, upland prairie and oak woodlands are now experiencing significant threat, with only 2% remaining in the Willamette Valley in small fragments (Hulse et al. 2002). These fragments are of high conservation value because of the rich biodiversity they support, including rare and endemic species, such as Delphinium leucophaeum (Oregon Department of Agriculture, 2020). Since 2010, Metro scientists and volunteers have collected phenology data on approximately 140 species of forbs and graminoids in regional oak prairie and woodlands. Phenology is the study of life-stage events in plants and animals, such as budbreak and senescence in flowering plants, and widely acknowledged as a sensitive indicator of environmental change (Parmesan 2007). Indeed, shifts in plant phenology have been observed over the last few decades as a result of climate change (Parmesan 2006). In oak systems, these changes have profound implications for plant community composition and diversity, as well as trophic interactions and general ecosystem function (Willis 2008). While the original intent of Metro’s phenology data-collection was to track long-term phenology trends, limitations in data collection methods have made such analysis difficult. Rather, these data are currently used to inform seasonal management decisions on Metro properties, such as when to collect seed for propagation and when to spray herbicide to control invasive species. Metro is now interested in fine-tuning their data-collection methods to better capture long-term phenology trends to guide future conservation strategies. Addressing the regional and global conservation issues of our time will require unprecedented collaboration. Phenology data collected on Metro properties is not only an important asset for Metro’s conservation plan, but holds potential to support broader research on a larger scale. As a leader in urban conservation, Metro is poised to make a meaningful scientific contribution by sharing phenology data with regional and national organizations. Data-sharing will benefit the common goal of conservation and create avenues for collaboration with other scientists and conservation practitioners (Rosemartin 2013). In order to support Metro’s ongoing conservation efforts in Oregon white oak systems, I have implemented a three-part master’s project. Part one of the project examines Metro’s previously collected phenology data, providing descriptive statistics and assessing the strengths and weaknesses of the methods by which the data were collected. Part two makes recommendations for improving future phenology data-collection methods, and includes recommendations for datasharing with regional and national organizations. Part three is a collection of scientific vouchers documenting key plant species in varying phases of phenology for Metro’s teaching herbarium. The purpose of these vouchers is to provide a visual tool for Metro staff and volunteers who rely on plant identification to carry out aspects of their job in plant conservation. Each component of this project addresses specific aspects of Metro’s conservation program, from day-to-day management concerns to long-term scientific inquiry.
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Monitoring Long-Term Cardiovascular Risk from Estrogen Use in Transgender Women - Evidence Update for Clinicians. Patient-Centered Outcomes Research Institute (PCORI), February 2002. http://dx.doi.org/10.25302/eu11.2020.2.

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A PCORI-funded study provides new information for primary care clinicians about the need to monitor long-term cardiovascular risks in transgender women receiving hormone therapy. Approximately 1.4 million transgender people live in the United States. Many transgender women pursue medical transition with hormone therapy including estrogen to align their bodies with their female gender identity. Evidence suggests that medical transition confers significant psychological benefits including reduced depression, anxiety, and suicidality and improved quality of life. However, the risks of using estrogen, including cardiovascular risks, are not well understood. Recent evidence on these risks can help inform decisions and improve care for transgender women who are currently using or formerly used estrogen.
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