Bücher: „Algorithmic decision systems“

1

Tijms, H. C. Stochastic models: An algorithmic approach. Chichester: Wiley, 1994.

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2

CAM: Algorithmen und Decision Support für die Fertigungssteuerung. Berlin: Springer-Verlag, 1989.

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3

A, Grundel Don, Murphey Robert und Pardalos P. M. 1954-, Hrsg. Theory and algorithms for cooperative systems. River Edge, NJ: World Scientific, 2004.

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4

Koukoudakis, Alexandros. Visualisation decision algorithm for temporal database management system. Manchester: UMIST, 1996.

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5

Chang, Hyeong Soo. Simulation-Based Algorithms for Markov Decision Processes. 2. Aufl. London: Springer London, 2013.

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6

Zilio, Daniel C. Physical database design decision algorithms and concurrent reorganization for parallel database systems. Toronto: University of Toronto, Dept. of Computer Science, 1998.

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7

Gdanskiy, Nikolay. Fundamentals of the theory and algorithms on graphs. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/978686.

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The textbook describes the main theoretical principles of graph theory, the main tasks to be solved using graph structures, and General methods of their solution and specific algorithms, with estimates of their complexity. I covered a lot of the examples given questions to test knowledge and tasks for independent decisions. Along with the control tasks to verify the theoretical training provided practical assignments to develop programs to study topics of graph theory. Meets the requirements of Federal state educational standards of higher education of the last generation. Designed for undergraduate and graduate programs, studying information technology, for in-depth training in analysis and design of systems of complex structure. Also the guide can be useful to specialists of the IT sphere in the study of algorithmic aspects of graph theory.

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8

Varlamov, Oleg. 18 examples of mivar expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1248446.

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Many years of research on mivar technologies of logical artificial intelligence have allowed us to create a new powerful, versatile and fast tool, which is called "multidimensional open gnoseological active net" — "multidimensional open gnoseological active net: MOGAN". This tool allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format, and it can be used to model cause-and-effect relationships in different subject areas and create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with "Big Knowledge". The reader, after studying this tutorial, you will be able to create mivar expert system with the help of CASMI Wi!Mi. Designed for students, bachelors, masters and postgraduate students studying artificial intelligence methods, as well as for users, experts and specialists, creating a system of information processing and management, mivar models, expert systems, automated control systems, systems of decision support and Recommender systems.

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9

Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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10

Antony, Richard T. Principles of data fusion automation. Boston: Artech House, 1995.

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11

Manichev, Vladimir, Valentina Glazkova und Кузьмина Анастасия. Numerical methods. The authentic and exact solution of the differential and algebraic equations in SAE systems of SAPR. ru: INFRA-M Academic Publishing LLC., 2016. http://dx.doi.org/10.12737/13138.

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In the manual classical numerical methods are considered and algorithms for the decision of systems of the ordinary differential equations (ODE), nonlinear and linear algebraic equations (NAU and LAU), and also ways of ensuring reliability and demanded accuracy of results of the decision. Ideas, which still not are stated are reflected in textbooks on calculus mathematics, namely: decision systems the ODE without reduction to a normal form of Cauchy resolved rather derivative, and refusal from any numerical an equivalent - nykh of transformations of the initial equations of mathematical models and is- the hodnykh of data because such transformations can change properties of models at a variation of coefficients in corresponding urav- neniyakh. It is intended for students, graduate students and teachers of higher education institutions in the direction of preparation "Informatics and computer facilities". The grant will also be useful for engineers and scientists on the corresponding specialties.

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12

Chuvikov, Dmitriy. Models and algorithms for reconstruction and examination of emergency events of road accidents based on logical artificial intelligence. 2. Aufl. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1220729.

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The purpose of the monograph is to create a methodology, combined expert and simulation models, as well as algorithms and software-modeling tools for reconstruction and examination of accident events for automating decision-making by an expert center employee. The methodology of combining and algorithms of joint work of an expert system based on logical artificial intelligence (mivar approach) and a simulation system for solving problems of reconstruction and examination of road accidents are developed; model reconstruction and examination of the accident in the formalism of the knowledge base bipartite oriented mivar nets, including analysis formulas braking qualities of the vehicle, determining the speed of a car's performance in terms of specific DTS, the formula for calculating different occasions: - slip car when braking, driving on curved sections of the road, hitting a car on the pedestrian in uniform motion and unlimited visibility; a method of generation of interfaces for designer expert systems based on the concept of mivar approach; special software in the form of expert systems "Analysis of road accident" in order to reduce the complexity of the process of calculating the disputed accidents, errors in the calculation and improve the accuracy and objectivity of the results obtained and the speed and quality of the calculations. It can be useful to specialists of expert institutions, insurance companies, educational institutions in the field of expertise, as well as unmanned vehicles in terms of objective analysis and examination of road accidents.

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13

Argyle, David J. Decision making in small animal oncology. Ames, Iowa: Blackwell Pub. Professional, 2008.

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14

Matthias, Ehrgott, Hrsg. Multiple criteria decision making for sustainable energy and transportation systems: Proceedings of the 19th International Conference on Multiple Criteria Decision Making, Auckland, New Zealand, 7th-12th January 2008. Heidelberg: Springer, 2010.

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15

Ziliaskopoulos, Athanasios K. Development of a prototype computerized transit information system to assist transit users with route planning decisions. Columbus, Ohio: Dept. of Civil and Environmental Engineering and Engineering Graphics, 1996.

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16

Otterlo, Martijn van. The logic of adaptive behavior: Knowledge representation and algorithms for adaptive sequential decision making under uncertainty in first-order and relational domains. Amsterdam: Ios Press, 2009.

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17

Otterlo, Martijn van. The logic of adaptive behavior: Knowledge representation and algorithms for adaptive sequential decision making under uncertainty in first-order and relational domains. Amsterdam: Ios Press, 2009.

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18

Lapko, V. A. Neparametricheskie kollektivy reshai︠u︡shchikh pravil. Novosibirsk: "Nauka", 2002.

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19

Grossklags, Jens. Decision and Game Theory for Security: Third International Conference, GameSec 2012, Budapest, Hungary, November 5-6, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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20

Intelligent systems and financial forecasting. London: Springer, 1997.

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21

Bernstein, Peter L. Against the gods: The remarkable story of risk. Toronto: John Wiley & Sons, Inc, 1996.

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22

Risuku: Kamigami e no hangyaku = Against the gods : the remarkable story of risk. Tōkyō: Nihon Keizai Shinbun, 1998.

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23

Bernstein, Peter L. Against the gods: The remarkable story of risk. New York: John Wiley & Sons, 1996.

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24

Minobrnauki, Rossiyskoy. Finance and Financial analysis. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1242227.

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The textbook systematizes basic knowledge in the field of finance, financial analysis and financial management, presented in their direct relationship and significance from the point of view of evaluation, diagnosis, forecasting and monitoring of the continuity of the organization's activities. It includes seven chapters grouped into three sections. The first section is devoted to the theoretical foundations of the organization's financial management, stakeholders and sources of the organization's activities. The second section discusses the basics of financial analysis, providing knowledge of the main directions, information base and methods of financial analysis, as well as allowing them to be applied reasonably, calculate and evaluate analytical indicators, determine the impact of globalization processes, various macro-and microfactors on the financial condition of the organization. The third section contains the basics of financial management, providing an understanding of the essence of the financial mechanism of the organization and algorithms for justifying decisions in the field of financial management. It complies with the federal state educational standards of higher education of the latest generation and provides the formation of basic competencies in the field of finance, financial management and financial analysis. For bachelor's, specialist's and master's students studying in the field of Economics, the system of additional professional education, training centers for advanced training of auditors and other financial market specialists, as well as for individual preparation of applicants for qualification certification and passing qualification exams.

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25

Yeung, Karen, und Martin Lodge, Hrsg. Algorithmic Regulation. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198838494.001.0001.

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Algorithms have become a central theme in contemporary policy and public discussion. The varied and potentially profound social implications of our increasing reliance on algorithms in daily life has attracted considerable interest in recent years, especially with the rising awareness of ‘big data’ and predictive analytics. One of the most vivid examples is the widespread concern about the use of algorithms to manipulate information and affect political life, at least since the US elections and the Brexit referendum. This book offers a critical exploration of algorithmic regulation, understood both as a means of coordinating and regulating social action and decision-making, as well as the need for institutional mechanisms through which the power of algorithms and algorithmic systems might themselves be regulated.

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26

Tijms, H. C. Stochastic Models: An Algorithmic Approach (Wiley Series in Probability and Statistics). John Wiley & Sons Inc, 1995.

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27

Tijms, H. C. Stochastic Models: An Algorithmic Approach (Wiley Series in Probability and Mathematical Statistics). John Wiley & Sons Ltd (Import), 1995.

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28

Algorithms in Decision Support Systems. MDPI, 2021. http://dx.doi.org/10.3390/books978-3-0365-0589-3.

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29

Vassiliadis, Vassilios, und Georgios Dounias. Algorithmic Trading based on Biologically Inspired Algorithms. Herausgegeben von Shu-Heng Chen, Mak Kaboudan und Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.11.

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The chapter discusses algorithmic trading, which refers to any automated process, consisting of a number of interconnected components, whose main aim is to perform financial transactions of any kind. Its chief advantage lies in the fact that human intervention is minimized to an acceptable extent. This is quite desirable because nowadays numerous factors affect financial decisions. Financial managers are able to deal with a limited amount of information. There are many ways to implement algorithmic trading systems. This chapter aims to highlight the efficiency of biologically inspired methodologies when incorporated in such systems. Biologically inspired intelligence comprises a range of algorithms whose common philosophy is based on the behavior of real-world, natural systems and networks. What is more, the performance of the applied nature-inspired intelligence (NII) methodologies is compared to traditional benchmark approaches such as the random portfolio construction.

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30

Theory and algorithms for cooperative systems. Singapore: World Scientific, 2004.

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31

Coomarasamy, Arri, und Jyotsna Pundir. Obstetrics: Evidence-Based Algorithms. Cambridge University Press, 2016.

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32

Chang, Hyeong Soo, Michael C. Fu und Jiaqiao Hu. Simulation-Based Algorithms for Markov Decision Processes. Springer, 2013.

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33

CAM: Algorithmen und Decision Support für die Fertigungssteuerung. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989.

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34

Mathematics for Algorithm and Systems Analysis. Dover Publications, 2005.

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35

Z, Hochberg, Hrsg. Practical algorithms in pediatric endocrinology. 2. Aufl. Basel: Karger, 2007.

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36

Zaspel, Joachim C. Automating pilot function performance assesssment using fuzzy systems and a genetic algorithm. 1997.

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37

Schmidt, Gunter. CAM: Algorithmen und Decision Support fur die Fertigungssteuerung (Betriebs- und Wirtschaftsinformatik). Springer-Verlag, 1989.

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38

Center, Langley Research, Hrsg. An algorithm for integrated subsystem embodiment and system synthesis. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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39

Center, Langley Research, Hrsg. An algorithm for integrated subsystem embodiment and system synthesis. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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40

Grundel, Don. Theory And Algorithms For Cooperative Systems (Series on Computers and Operations Research). World Scientific Publishing Company, 2004.

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41

Chen, Shu-Heng, Mak Kaboudan und Ye-Rong Du, Hrsg. The Oxford Handbook of Computational Economics and Finance. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.001.0001.

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Being published as a celebration of the 60th anniversary of John von Neumann’s “Theory of Self-Reproducing Automata,” this handbook attempts to provide a unique reflection on the nature of computational economics and finance (CEF) in light of natural computationalism. We restructure CEF by including both nature-inspired computing and natural computing. This new framework allows us to have a view of CEF much broader than just the conventional algorithmic consideration. The book begins with a historical review of computational economics (CE), tracing its history far back to the era of analog computing. In these early days, advancements were mainly made using the idea of natural computing, and the subjects pursued by CE were the computing system as a whole, not just numerical computing. The handbook then is organized by distinguishing computing from computing systems. Six chapters (Chapters 2 to 7) are devoted to the former. They together present a review on the recent progresses in CE, as illustrated by the computation of rational expectations, general equilibrium, risk, and volatility. The subsequent 16 chapters are devoted to the computing-systemic view of CE, including natural-inspired computing (Chapters 8 to 12) and network, agent-based computing and neural computing (Chapters 13 to 23). In addition to providing alternative approaches to forecasting, investment strategies and risk management, etc., they enable us to have a 'natural' or more realistic description of the economy, starting from its decision makers; hence, market-design or policy-design issues involving different levels of the economy, be microscopic, mesoscopic and macroscopic, can be simultaneously addressed and coherently integrated. The handbook concludes with a chapter on what we may hope from CE by providing an in-depth review on the epistemological aspects of computation.

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42

Aziz, Nazrina, Syariza Abdul-Rahman und Norhaslina Zainal Abidin, Hrsg. Recent Applications in Quantitative Methods and Information Technology. UUM Press, 2019. http://dx.doi.org/10.32890/9789672210269.

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This book is a guide for researchers who are involved in statistical, mathematical, information technology and decision science analyses. The purpose of the book is to allow readers to get research ideas on a wide range of topics, such as sampling plans, capital budgeting, completion time in production line, searching pattern for mobile cache replacement policy, home security system with biometric finger print and web service technology. The analyses in each chapter are explained in detail with samples of real applications in daily life to assist readers to appreciate theoretical, algorithm and mathematical formulations. Prior to reading this book, readers are advised to have some basic foundation in statistical sampling, tabu search approach, neural network, algorithms, and mathematical formulation. This book will be beneficial to students and researchers who are looking for research topic of the research and how problems can be solved using an applied method.

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43

Argyle, David J., Malcolm J. Brearley und Michelle M. Turek. Decision Making in Small Animal Oncology. Wiley & Sons, Incorporated, John, 2009.

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44

Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World. Wiley, 2014.

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45

McNeill, Fiona, und Carlos Andre Reis Pinheiro. Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World. Wiley & Sons, Incorporated, John, 2014.

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46

McNeill, Fiona, und Carlos Andre Reis Pinheiro. Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World. Wiley & Sons, Incorporated, John, 2014.

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47

Deboeck, Guido J. Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets. Wiley & Sons, Incorporated, John, 2007.

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48

Guido, Deboeck, Hrsg. Trading on the edge: Neural, genetic, and fuzzy systems for chaotic financial markets. New York: Wiley, 1994.

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49

Deboeck, Guido J. Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets (Wiley Finance). Wiley, 1994.

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50

Suran, Goonatilake, und Treleaven P. C, Hrsg. Intelligent systems for finance and business. Chichester: Wiley, 1995.

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Bücher zum Thema „Algorithmic decision systems“

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