Uncertainty And Intelligent Systems

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Uncertainty and Intelligent Systems

Author : Bernadette Bouchon
Publisher : Springer Science & Business Media
ISBN 13 : 9783540194026
Total Pages : 420 pages
Book Rating : 4.1/5 (94 download)

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Book Synopsis Uncertainty and Intelligent Systems by : Bernadette Bouchon

Download or read book Uncertainty and Intelligent Systems written by Bernadette Bouchon and published by Springer Science & Business Media. This book was released on 1988-06-08 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Artificial Intelligence with Uncertainty

Author : Deyi Li
Publisher : CRC Press
ISBN 13 : 1498776272
Total Pages : 290 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Artificial Intelligence with Uncertainty by : Deyi Li

Download or read book Artificial Intelligence with Uncertainty written by Deyi Li and published by CRC Press. This book was released on 2017-05-18 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops a framework that shows how uncertainty in Artificial Intelligence (AI) expands and generalizes traditional AI. It explores the uncertainties of knowledge and intelligence. The authors focus on the importance of natural language – the carrier of knowledge and intelligence, and introduce efficient physical methods for data mining amd control. In this new edition, we have more in-depth description of the models and methods, of which the mathematical properties are proved strictly which make these theories and methods more complete. The authors also highlight their latest research results.

Probabilistic Reasoning in Intelligent Systems

Author : Judea Pearl
Publisher : Elsevier
ISBN 13 : 0080514898
Total Pages : 552 pages
Book Rating : 4.0/5 (85 download)

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Book Synopsis Probabilistic Reasoning in Intelligent Systems by : Judea Pearl

Download or read book Probabilistic Reasoning in Intelligent Systems written by Judea Pearl and published by Elsevier. This book was released on 2014-06-28 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Uncertainty and Intelligent information Systems

Author :
Publisher :
ISBN 13 : 9814471798
Total Pages : pages
Book Rating : 4.8/5 (144 download)

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Book Synopsis Uncertainty and Intelligent information Systems by :

Download or read book Uncertainty and Intelligent information Systems written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Uncertainty and Intelligent Systems

Author : Bernadette Bouchon
Publisher :
ISBN 13 : 9783662210659
Total Pages : 420 pages
Book Rating : 4.2/5 (16 download)

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Book Synopsis Uncertainty and Intelligent Systems by : Bernadette Bouchon

Download or read book Uncertainty and Intelligent Systems written by Bernadette Bouchon and published by . This book was released on 2014-01-15 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Uncertainty and Intelligent Information Systems

Author : Ronald R. Yager
Publisher : World Scientific
ISBN 13 : 981279235X
Total Pages : 537 pages
Book Rating : 4.8/5 (127 download)

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Book Synopsis Uncertainty and Intelligent Information Systems by : Ronald R. Yager

Download or read book Uncertainty and Intelligent Information Systems written by Ronald R. Yager and published by World Scientific. This book was released on 2008 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Intelligent Systems

Author : Crina Grosan
Publisher : Springer Science & Business Media
ISBN 13 : 364221004X
Total Pages : 450 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Intelligent Systems by : Crina Grosan

Download or read book Intelligent Systems written by Crina Grosan and published by Springer Science & Business Media. This book was released on 2011-07-29 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. This book comprising of 17 chapters offers a step-by-step introduction (in a chronological order) to the various modern computational intelligence tools used in practical problem solving. Staring with different search techniques including informed and uninformed search, heuristic search, minmax, alpha-beta pruning methods, evolutionary algorithms and swarm intelligent techniques; the authors illustrate the design of knowledge-based systems and advanced expert systems, which incorporate uncertainty and fuzziness. Machine learning algorithms including decision trees and artificial neural networks are presented and finally the fundamentals of hybrid intelligent systems are also depicted. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques, machine learning and data mining would find the comprehensive coverage of this book invaluable.

Intelligent Systems for Information Processing: From Representation to Applications

Author : B. Bouchon-Meunier
Publisher : Elsevier
ISBN 13 : 0080525652
Total Pages : 488 pages
Book Rating : 4.0/5 (85 download)

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Book Synopsis Intelligent Systems for Information Processing: From Representation to Applications by : B. Bouchon-Meunier

Download or read book Intelligent Systems for Information Processing: From Representation to Applications written by B. Bouchon-Meunier and published by Elsevier. This book was released on 2003-11-07 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Intelligent Systems for Engineers and Scientists

Author : Adrian A. Hopgood
Publisher : CRC Press
ISBN 13 : 1466516178
Total Pages : 455 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Intelligent Systems for Engineers and Scientists by : Adrian A. Hopgood

Download or read book Intelligent Systems for Engineers and Scientists written by Adrian A. Hopgood and published by CRC Press. This book was released on 2012-02-02 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: The third edition of this bestseller examines the principles of artificial intelligence and their application to engineering and science, as well as techniques for developing intelligent systems to solve practical problems. Covering the full spectrum of intelligent systems techniques, it incorporates knowledge-based systems, computational intelligence, and their hybrids. Using clear and concise language, Intelligent Systems for Engineers and Scientists, Third Edition features updates and improvements throughout all chapters. It includes expanded and separated chapters on genetic algorithms and single-candidate optimization techniques, while the chapter on neural networks now covers spiking networks and a range of recurrent networks. The book also provides extended coverage of fuzzy logic, including type-2 and fuzzy control systems. Example programs using rules and uncertainty are presented in an industry-standard format, so that you can run them yourself. The first part of the book describes key techniques of artificial intelligence—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), frames, objects, agents, symbolic learning, case-based reasoning, genetic algorithms, optimization algorithms, neural networks, hybrids, and the Lisp and Prolog languages. The second part describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. The author provides sufficient detail to help you develop your own intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, this book provides you with detailed and up-to-date guidance. Check out the significantly expanded set of free web-based resources that support the book at: http://www.adrianhopgood.com/aitoolkit/

Uncertainty in Artificial Intelligence

Author : David Heckerman
Publisher : Morgan Kaufmann
ISBN 13 : 1483214516
Total Pages : 552 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Uncertainty in Artificial Intelligence by : David Heckerman

Download or read book Uncertainty in Artificial Intelligence written by David Heckerman and published by Morgan Kaufmann. This book was released on 2014-05-12 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Intelligent Systems in Oil Field Development under Uncertainty

Author : Marco A. C. Pacheco
Publisher : Springer Science & Business Media
ISBN 13 : 3540929991
Total Pages : 296 pages
Book Rating : 4.5/5 (49 download)

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Book Synopsis Intelligent Systems in Oil Field Development under Uncertainty by : Marco A. C. Pacheco

Download or read book Intelligent Systems in Oil Field Development under Uncertainty written by Marco A. C. Pacheco and published by Springer Science & Business Media. This book was released on 2009-04-27 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 and Vagueness in Knowledge Based Systems

Author : Rudolf Kruse
Publisher : Springer Science & Business Media
ISBN 13 : 3642767028
Total Pages : 495 pages
Book Rating : 4.6/5 (427 download)

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Book Synopsis Uncertainty and Vagueness in Knowledge Based Systems by : Rudolf Kruse

Download or read book Uncertainty and Vagueness in Knowledge Based Systems written by Rudolf Kruse and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary aim of this monograph is to provide a formal framework for the representation and management of uncertainty and vagueness in the field of artificial intelligence. It puts particular emphasis on a thorough analysis of these phenomena and on the development of sound mathematical modeling approaches. Beyond this theoretical basis the scope of the book includes also implementational aspects and a valuation of existing models and systems. The fundamental ambition of this book is to show that vagueness and un certainty can be handled adequately by using measure-theoretic methods. The presentation of applicable knowledge representation formalisms and reasoning algorithms substantiates the claim that efficiency requirements do not necessar ily require renunciation of an uncompromising mathematical modeling. These results are used to evaluate systems based on probabilistic methods as well as on non-standard concepts such as certainty factors, fuzzy sets or belief functions. The book is intended to be self-contained and addresses researchers and practioneers in the field of knowledge based systems. It is in particular suit able as a textbook for graduate-level students in AI, operations research and applied probability. A solid mathematical background is necessary for reading this book. Essential parts of the material have been the subject of courses given by the first author for students of computer science and mathematics held since 1984 at the University in Braunschweig.

Uncertainty in Artificial Intelligence

Author : Didier J. Dubois
Publisher : Morgan Kaufmann
ISBN 13 : 1483282872
Total Pages : 378 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Uncertainty in Artificial Intelligence by : Didier J. Dubois

Download or read book Uncertainty in Artificial Intelligence written by Didier J. Dubois and published by Morgan Kaufmann. This book was released on 2014-05-12 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference (1992) covers the papers presented at the Eighth Conference on Uncertainty in Artificial Intelligence, held at Stanford University on July 17-19, 1992. The book focuses on the processes, methodologies, technologies, and approaches involved in artificial intelligence. The selection first offers information on Relative Evidential Support (RES), modal logics for qualitative possibility and beliefs, and optimizing causal orderings for generating DAGs from data. Discussions focus on reversal, swap, and unclique operators, modal representation of possibility, and beliefs and conditionals. The text then examines structural controllability and observability in influence diagrams, lattice-based graded logic, and dynamic network models for forecasting. The manuscript takes a look at reformulating inference problems through selective conditioning, entropy and belief networks, parallelizing probabilistic inference, and a symbolic approach to reasoning with linguistic quantifiers. The text also ponders on sidestepping the triangulation problem in Bayesian net computations; exploring localization in Bayesian networks for large expert systems; and expressing relational and temporal knowledge in visual probabilistic networks. The selection is a valuable reference for researchers interested in artificial intelligence.

Uncertainty in Artificial Intelligence

Author : MKP
Publisher : Elsevier
ISBN 13 : 1483298604
Total Pages : 625 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Uncertainty in Artificial Intelligence by : MKP

Download or read book Uncertainty in Artificial Intelligence written by MKP and published by Elsevier. This book was released on 2014-06-28 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty Proceedings 1994

Uncertainty in Artificial Intelligence 4

Author : T.S. Levitt
Publisher : Elsevier
ISBN 13 : 1483296547
Total Pages : 422 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Uncertainty in Artificial Intelligence 4 by : T.S. Levitt

Download or read book Uncertainty in Artificial Intelligence 4 written by T.S. Levitt and published by Elsevier. This book was released on 2014-06-28 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clearly illustrated in this volume is the current relationship between Uncertainty and AI. It has been said that research in AI revolves around five basic questions asked relative to some particular domain: What knowledge is required? How can this knowledge be acquired? How can it be represented in a system? How should this knowledge be manipulated in order to provide intelligent behavior? How can the behavior be explained? In this volume, all of these questions are addressed. From the perspective of the relationship of uncertainty to the basic questions of AI, the book divides naturally into four sections which highlight both the strengths and weaknesses of the current state of the relationship between Uncertainty and AI.

Uncertainty in Artificial Intelligence

Author : Bruce D'Ambrosio
Publisher : Elsevier
ISBN 13 : 1483298566
Total Pages : 445 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Uncertainty in Artificial Intelligence by : Bruce D'Ambrosio

Download or read book Uncertainty in Artificial Intelligence written by Bruce D'Ambrosio and published by Elsevier. This book was released on 2014-06-28 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty Proceedings 1991

Intelligent Systems and Financial Forecasting

Author : Jason Kingdon
Publisher : Springer Science & Business Media
ISBN 13 : 144710949X
Total Pages : 233 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Intelligent Systems and Financial Forecasting by : Jason Kingdon

Download or read book Intelligent Systems and Financial Forecasting written by Jason Kingdon and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fundamental objective of Artificial Intelligence (AI) is the creation of in telligent computer programs. In more modest terms AI is simply con cerned with expanding the repertoire of computer applications into new domains and to new levels of efficiency. The motivation for this effort comes from many sources. At a practical level there is always a demand for achieving things in more efficient ways. Equally, there is the technical challenge of building programs that allow a machine to do something a machine has never done before. Both of these desires are contained within AI and both provide the inspirational force behind its development. In terms of satisfying both of these desires there can be no better example than machine learning. Machines that can learn have an in-built effi ciency. The same software can be applied in many applications and in many circumstances. The machine can adapt its behaviour so as to meet the demands of new, or changing, environments without the need for costly re-programming. In addition, a machine that can learn can be ap plied in new domains with the genuine potential for innovation. In this sense a machine that can learn can be applied in areas where little is known about possible causal relationships, and even in circumstances where causal relationships are judged not to exist. This last aspect is of major significance when considering machine learning as applied to fi nancial forecasting.