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Books on the topic 'Intelligent model'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Levitt, Raymond E. Merging artificial intelligence with CAD: Intelligent, model-based engineering. Raleigh, N.C: Dept. of Civil Engineering, College of Engineering, North Carolina State University, 1991.

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2

Autonomous, model-based diagnosis agents. Boston: Kluwer, 1998.

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3

Magedanz, Thomas. An integrated management model for intelligent networks. Munich: R. Oldenbourg, 1994.

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4

Su, Xiaojie, Yao Wen, Yue Yang, and Peng Shi. Intelligent Control, Filtering and Model Reduction Analysis for Fuzzy-Model-Based Systems. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-81214-0.

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5

Shaw, Michael. Learning model management knowledge in intelligent decision support systems. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1988.

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6

Akinsola, Abiodun Olanrewaju. An intelligent model of variations' contingency on constructions projects. Wolverhampton: University of Wolverhampton, 1997.

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7

Odincov, Boris. Models and intelligent systems. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1060845.

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The monograph consists of three chapters, the first of which outlines the theoretical foundations of intelligent information systems. Special attention is paid to the disclosure of the term "model" as the intended meaning depends on the understanding of the material. Introduces and examines the new concepts such as the associative and intuitive knowledge while in the creation of intellectual information systems are not used. The second Chapter contains the analysis of problems of development of artificial intelligence (AI), developed in two directions: classical and statistical. Discusses difficulties in the development of the classical approach, associated with identifying the meaning of words, phrases, text, and formulating thoughts. The analysis of problems arising in the play of imagination and insight, machine understanding of natural language texts, play, verbalization and reflection. The third Chapter contains examples of the development of intelligent information systems and technologies in practice of management of economic objects. Theoretical bases of construction of information robots designed to support the task hierarchy of the knowledge base and generating control regulations. The technology of their creation and application in the management of the business efficiency of enterprise business processes and its investment activities. Focused on researchers and developers, AI and intelligent information systems, as well as graduate students and faculty in related academic disciplines.

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8

Balakirsky, Stephen. Vehicle level world model manager interface description. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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9

Balakirsky, Stephen. Vehicle level world model manager interface description. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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10

Balakirsky, Stephen. Vehicle level world model manager interface description. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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11

G, Mink Oscar, ed. Open organizations: A model for effectiveness, renewal, and intelligent change. San Francisco: Jossey-Bass, 1994.

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12

1969-, Catania Barbara, and Zarri Gian Piero, eds. Intelligent database systems. Harlow, England: Addison-Wesley, 2001.

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13

Dailey, Daniel J. Smart Trek: A model deployment initiative. [Olympia, Wash.]: Washington State Dept. of Transportation, 2001.

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14

Schroeder, Michael. Autonomous, Model-Based Diagnosis Agents. Boston, MA: Springer US, 1998.

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15

D, Parsons Simon, and SpringerLink (Online service), eds. Markov Decision Processes and the Belief-Desire-Intention Model: Bridging the Gap for Autonomous Agents. New York, NY: The Author(s), 2011.

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16

Hoppe, William Charles. Cognitive modeling and the evolution of the student model in intelligent tutoring systems. Monterey, Calif: Naval Postgraduate School, 1992.

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17

Koning, Kees de. Model-based reasoning about learner behaviour. Amsterdam: IOS Press, 1997.

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18

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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19

Dede, Christopher. Designing a training tool for imaging mental models. [Houston, Tex.?]: Research Institute for Computing and Information Systems, University of Houston, Clear Lake, 1990.

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20

Ganapathi, Geetha, Arumugam Subramaniam, Manuel Graña, Suresh Balusamy, Rajamanickam Natarajan, and Periakaruppan Ramanathan, eds. Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0716-4.

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21

Balusamy, Suresh, Alexander N. Dudin, Manuel Graña, A. Kaja Mohideen, N. K. Sreelaja, and B. Malar, eds. Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9700-8.

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22

Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.

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Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. 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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23

Raman, Indhumathi, Poonthalir Ganesan, Venkatasamy Sureshkumar, and Latha Ranganathan, eds. Computational Intelligence, Cyber Security and Computational Models. Recent Trends in Computational Models, Intelligent and Secure Systems. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15556-7.

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24

Susan, Haller, Kobsa Alfred, and McRoy Susan, eds. Computational models of mixed-initiative interaction. Dordrecht: Kluwer Academic, 1999.

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25

Edelkamp, Stefan, and Alessio Lomuscio, eds. Model Checking and Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74128-2.

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26

Peled, Doron A., and Michael J. Wooldridge, eds. Model Checking and Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00431-5.

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27

van der Meyden, Ron, and Jan-Georg Smaus, eds. Model Checking and Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20674-0.

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28

Pap, Endre, ed. Intelligent Systems: Models and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33959-2.

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29

A deduction model of belief. London: Pitman, 1986.

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30

Gammerman, Alex, ed. Causal Models and Intelligent Data Management. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58648-4.

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31

Kołodziej, Joanna. Advances in Intelligent Modelling and Simulation: Artificial Intelligence-Based Models and Techniques in Scalable Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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32

Keller, Richard M. Artificial intelligence support for scientific model-building. [Moffett Field, CA]: NASA, Ames Research Center, Artificial Intelligence Research Branch, 1992.

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33

Keller, Richard. Artificial intelligence support for scientific model-building. [Moffett Field, CA]: NASA, Ames Research Center, Artificial Intelligence Research Branch, 1992.

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34

Baya'a, Nimer Fayez. Semi-intelligent computer assisted instruction model. 1987.

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35

Baya'a, Nimer Fayez. Semi-intelligent computer assisted instruction model. 1987.

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36

Shi, Peng, Yue Yang, Xiaojie Su, and Yao Wen. Intelligent Control, Filtering and Model Reduction Analysis for Fuzzy-Model-Based Systems. Springer International Publishing AG, 2021.

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37

Shi, Peng, Yue Yang, Xiaojie Su, and Yao Wen. Intelligent Control, Filtering and Model Reduction Analysis for Fuzzy-Model-Based Systems. Springer International Publishing AG, 2022.

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38

Non-Axiomatic Logic: A Model of Intelligent Reasoning. World Scientific Publishing Co Pte Ltd, 2013.

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39

Borges, Marcos De Souza. Intelligent Shepherding: The Model of Spiritual Counseling for Deliverance. Balboa Pr, 2017.

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40

Zhang, Chengqi, Qingfeng Chen, and Baoshan Chen. Intelligent Strategies for Pathway Mining: Model and Pattern Identification. Springer London, Limited, 2013.

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41

Intelligent Strategies For Pathway Mining Model And Pattern Identification. Springer International Publishing AG, 2013.

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42

BORGES, MARCOS DE SOUZA. Intelligent Shepherding: The Model of Spiritual Counseling for Deliverance. BalboaPress, 2017.

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43

Sacra, Albus James, and National Institute of Standards and Technology (U.S.), eds. An intelligent systems architecture for manufacturing (ISAM): A reference model architecture for intelligent manufacturing systems. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2002.

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44

Graen, George, and Julio Canedo. Intelligent Startup: A New Model of Coordination for Tomorrow's Leaders. Ballast Books, 2022.

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45

United States. Federal Highway Administration., ed. Interactive Highway Safety Design Model: IHSDM. [Washington, D.C.]: U.S. Dept. of Transportation, Federal Highway Administration, 2000.

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46

Zheng, Xu, Vijayan Sugumaran, and Huiyu Zhou. Application of Intelligent Systems in Multi-modal Information Analytics: Proceedings of the 2020 International Conference on Multi-model Information ... in Intelligent Systems and Computing ). Springer, 2020.

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47

Zheng, Xu, Vijayan Sugumaran, and Huiyu Zhou. Application of Intelligent Systems in Multi-modal Information Analytics: Proceedings of the 2020 International Conference on Multi-model Information ... in Intelligent Systems and Computing ). Springer, 2020.

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48

Kohanov, Linda, and Teri Schnaubelt. Five Roles of a Master Herder: A Revolutionary Model for Socially Intelligent Leadership. Brilliance Audio, 2016.

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49

Sacra, Albus James, and National Institute of Standards and Technology (U.S.), eds. Concept for a reference model Architecture for Real-Time Intelligent Control Systems (ARTICS). Gaithersburg, MD: U.S. Dept. of Commerce, National Institute of Standards and Technology, 1990.

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50

Fung, Richard Ying Kit. An intelligent hybrid model for customer requirements interpretation and product design targets determination. 1997.

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