Uncertainty And Intelligent Systems
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Uncertainty And Intelligent Systems
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Author : Bernadette Bouchon
language : en
Publisher: Springer Science & Business Media
Release Date : 1988-06-08
Uncertainty And Intelligent Systems written by Bernadette Bouchon and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988-06-08 with Computers categories.
This book contains the papers presented at the 2nd IPMU Conference, held in Urbino (Italy), on July 4-7, 1988. The theme of the conference, Management of Uncertainty and Approximate Reasoning, is at the heart of many knowledge-based systems and a number of approaches have been developed for representing these types of information. The proceedings of the conference provide, on one hand, the opportunity for researchers to have a comprehensive view of recent results and, on the other, bring to the attention of a broader community the potential impact of developments in this area for future generation knowledge-based systems. The main topics are the following: frameworks for knowledge-based systems: representation scheme, neural networks, parallel reasoning schemes; reasoning techniques under uncertainty: non-monotonic and default reasoning, evidence theory, fuzzy sets, possibility theory, Bayesian inference, approximate reasoning; information theoretical approaches; knowledge acquisition and automated learning.
Uncertainty And Intelligent Systems
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Author : Bernadette Bouchon
language : en
Publisher:
Release Date : 2014-01-15
Uncertainty And Intelligent Systems written by Bernadette Bouchon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.
Uncertainty And Intelligent Information Systems
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Author :
language : en
Publisher:
Release Date :
Uncertainty And Intelligent Information Systems written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Uncertainty And Intelligent Information Systems
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Author : Ronald R. Yager
language : en
Publisher: World Scientific
Release Date : 2008
Uncertainty And Intelligent Information Systems written by Ronald R. Yager and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computers categories.
Intelligent systems are necessary to handle modern computer-based technologies managing information and knowledge. This book discusses the theories required to help provide solutions to difficult problems in the construction of intelligent systems. Particular attention is paid to situations in which the available information and data may be imprecise, uncertain, incomplete or of a linguistic nature. The main aspects of clustering, classification, summarization, decision making and systems modeling are also addressed. Topics covered in the book include fundamental issues in uncertainty, the rapidly emerging discipline of information aggregation, neural networks, Bayesian networks and other network methods, as well as logic-based systems.
Probabilistic Reasoning In Intelligent Systems
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Author : Judea Pearl
language : en
Publisher: Elsevier
Release Date : 2014-06-28
Probabilistic Reasoning In Intelligent Systems written by Judea Pearl and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
Challenging Problems And Solutions In Intelligent Systems
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Author : Guy de Trė
language : en
Publisher: Springer
Release Date : 2016-03-25
Challenging Problems And Solutions In Intelligent Systems written by Guy de Trė and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-25 with Technology & Engineering categories.
This volume presents recent research, challenging problems and solutions in Intelligent Systems– covering the following disciplines: artificial and computational intelligence, fuzzy logic and other non-classic logics, intelligent database systems, information retrieval, information fusion, intelligent search (engines), data mining, cluster analysis, unsupervised learning, machine learning, intelligent data analysis, (group) decision support systems, intelligent agents and multi-agent systems, knowledge-based systems, imprecision and uncertainty handling, electronic commerce, distributed systems, etc. The book defines a common ground for sometimes seemingly disparate problems and addresses them by using the paradigm of broadly perceived intelligent systems. It presents a broad panorama of a multitude of theoretical and practical problems which have been successfully dealt with using the paradigm of intelligent computing.
Intelligent Systems In Oil Field Development Under Uncertainty
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Author : Marco A. C. Pacheco
language : en
Publisher: Springer
Release Date : 2009-01-30
Intelligent Systems In Oil Field Development Under Uncertainty written by Marco A. C. Pacheco and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-01-30 with Technology & Engineering categories.
The decision to invest in oil field development is an extremely complex problem, even in the absence of uncertainty, due to the great number of technological alternatives that may be used, to the dynamic complexity of oil reservoirs - which involves mul- phase flows (oil, gas and water) in porous media with phase change, and to the c- plicated combinatorial optimization problem of choosing the optimal oil well network, that is, choosing the number and types of wells (horizontal, vertical, directional, m- tilateral) required for draining oil from a field with a view to maximizing its economic value. This problem becomes even more difficult when technical uncertainty and e- nomic uncertainty are considered. The former are uncertainties regarding the existence, volume and quality of a reservoir and may encourage an investment in information before the field is developed, in order to reduce these uncertainties and thus optimize the heavy investments required for developing the reservoir. The economic or market uncertainties are associated with the general movements of the economy, such as oil prices, gas demand, exchange rates, etc. , and may lead decision-makers to defer - vestments and wait for better market conditions. Choosing the optimal investment moment under uncertainty is a complex problem which traditionally involves dynamic programming tools and other techniques that are used by the real options theory.
Uncertainty In Computational Intelligence Based Decision Making
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Author : Ali Ahmadian
language : en
Publisher: Elsevier
Release Date : 2024-09-23
Uncertainty In Computational Intelligence Based Decision Making written by Ali Ahmadian and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-23 with Computers categories.
Uncertainty in Computational Intelligence-Based Decision-Making focuses on techniques for reasoning and decision-making under uncertainty that are used to solve issues in artificial intelligence (AI). It covers a wide range of subjects, including knowledge acquisition and automated model construction, pattern recognition, machine learning, natural language processing, decision analysis, and decision support systems, among others. The first chapter of this book provides a thorough introduction to the topics of causation in Bayesian belief networks, applications of uncertainty, automated model construction and learning, graphic models for inference and decision making, and qualitative reasoning. The following chapters examine the fundamental models of computational techniques, computational modeling of biological and natural intelligent systems, including swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems, and evolutionary computation. They also examine decision making and analysis, expert systems, and robotics in the context of artificial intelligence and computer science. Provides readers a thorough understanding of the uncertainty that arises in artificial intelligence (AI), computational intelligence (CI) paradigms, and algorithms Encourages readers to put concepts into practice and solve complex real-world problems using CI development frameworks like decision support systems and visual decision design Provides a comprehensive overview of the techniques used in computational intelligence, uncertainty, and decision
Intelligent Systems For Information Processing From Representation To Applications
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Author : B. Bouchon-Meunier
language : en
Publisher: Elsevier
Release Date : 2003-11-07
Intelligent Systems For Information Processing From Representation To Applications written by B. Bouchon-Meunier and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-11-07 with Mathematics categories.
Intelligent systems are required to enhance the capacities being made available to us by the internet and other computer based technologies. The theory necessary to help providing solutions to difficult problems in the construction of intelligent systems are discussed. In particular, attention is paid to situations in which the available information and data may be imprecise, uncertain, incomplete or of a linguistic nature. Various methodologies to manage such information are discussed. Among these are the probabilistic, possibilistic, fuzzy, logical, evidential and network-based frameworks. One purpose of the book is not to consider these methodologies separately, but rather to consider how they can be used cooperatively to better represent the multiplicity of modes of information. Topics in the book include representation of imperfect knowledge, fundamental issues in uncertainty, reasoning, information retrieval, learning and mining, as well as various applications. Key Features: • Tools for construction of intelligent systems • Contributions by world leading experts • Fundamental issues and applications • New technologies for web searching • Methods for modeling uncertain information • Future directions in web technologies • Transversal to methods and domains
Uncertainty In Artificial Intelligence
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Author : David Heckerman
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-05-12
Uncertainty In Artificial Intelligence written by David Heckerman and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-12 with Computers categories.
Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.