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Wednesday, August 5, 2020 | History

6 edition of Uncertainty and Intelligent Systems found in the catalog.

Uncertainty and Intelligent Systems

2nd International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems IPMU ... (Lecture Notes in Computer Science)

  • 337 Want to read
  • 2 Currently reading

Published by Springer .
Written in English

    Subjects:
  • Knowledge-based systems / expert systems,
  • Artificial Intelligence - General,
  • Computers / Artificial Intelligence,
  • Computers - General Information

  • Edition Notes

    ContributionsBernadette Bouchon (Editor), Lorenza Saitta (Editor), Ronald R. Yager (Editor)
    The Physical Object
    FormatPaperback
    Number of Pages408
    ID Numbers
    Open LibraryOL12772774M
    ISBN 103540194029
    ISBN 109783540194026

    This book constitutes the refereed proceedings of the 9th International Symposium on Methodologies for Intelligent Systems, ISMIS '96, held in Zakopane, Poland, in June The 53 revised full papers presented were selected from a total of submissions; also included are 10 invited papers by leading experts surveying the state of the art. For book reviews, please feel free to contact Adnan Masood at [email protected] Elegant Discussion On Probabilistic Reasoning And Uncertainty, Pearl's "Probabilistic Reasoning in Intelligent Systems" is reasoning and all things related inference. As the author says, "This book is a culmination of an investigation intoFile Size: KB.

    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. Free Download Scientific Methods for the Treatment of Uncertainty in Social Sciences (Advances in Intelligent Systems and Computing) ; Scientific Methods for the Treatment of Uncertainty in Social Sciences (Advances in Intelligent Systems and Computing) Pdf , , , , Download.

    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,/5.   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.5/5(1).


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Uncertainty and Intelligent Systems Download PDF EPUB FB2

This book contains the papers presented at the 2nd IPMU Conference, held in Urbino (Italy), on JulyThe 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.

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 Intelligent systems are necessary to handle modern computer-based technologies 4/5(1).

International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (2nd: Urbino, Italy).

Uncertainty and intelligent systems. Berlin ; New York: Springer-Verlag, © (OCoLC) Material Type: Conference publication, Internet resource: Document Type: Book, Internet Resource. Uncertainty and Intelligent Information Systems [Ronlad R Yager, Bernadette Bouchon-Meunier, Christophe Marsala, Maria Rifqi, Enrique Miranda] on *FREE* shipping on qualifying offers.

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. 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 by: 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. This book contains the papers presented at the 2nd IPMU Conference, held in Urbino (Italy), on JulyThe 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.

Examines the representation of uncertainty in intelligent systems. Areas covered include fuzzy set theory, probability theory and mathematical theory of evidence.

Sections are devoted to reasoning with uncertainty, network-based methods, fuzzy set methods and analyzing uncertain measures. Intelligent systems include a range of techniques (e.g. neural networks, fuzzy logic/systems, genetic algorithms and genetic programming, expert systems, case-based reasoning, etc.) that operate.

Shafer's theory of belief and the Bayesian theory of probability are two alternative and mutually inconsistent approaches toward modelling uncertainty in artificial intelligence.

To help reduce the conflict between these two approaches, this paper reexamines expected utility theory — from which Bayesian probability theory is derived.

Chapter 1 UNCERTAINTY IN AI SYSTEMS AN OVERVIEW Publisher Summary This chapter highlights the basic AI paradigms of dealing with uncertainty and describes the unique qualitative features that make probability - Selection from Probabilistic Reasoning in Intelligent Systems [Book].

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.

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper.

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.

This book is a collection of articles on the technologies needed for the construction of intelligent and information systems, and particularly on the role of uncertainty. The articles, written by some of the world's leading experts, cover the management of uncertainty and the modeling of intelligent and information systems.

Intelligent Systems (IS) offer to managers and decision makers the best solutions for complex applications, normally considered difficult, very restrictive or even impossible.

The use of such techniques leads to a revolutionary process which has a significant impact in the business management strategy, by providing on time, correct information. 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. Design of Logic-based Intelligent Systems develops principles and methods for constructing intelligent systems for complex tasks that are readily done by humans but are difficult for machines.

Current Artificial Intelligence (AI) approaches rely on various constructs and methods (production rules, neural nets, support vector machines, fuzzy.

A computational perspective on uncertainty has two aspects: the explicit rep-resentation of uncertainty and the algorithmic manipulation of this representation so as to transform and (often) to reduce uncertainty. In his seminal book, Probabilistic Reasoning in Intelligent Systems, Pearl showed that these aspects are intimately Size: KB.

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only do ebook promotions online and we does not distribute any free download of ebook on this site. Deal with information and uncertainty properly and efficiently using tools emerging from generalized information theory Uncertainty and Information: Foundations of Generalized Information Theory contains comprehensive and up-to-date coverage of results that have emerged from a research program begun by the author in the early s under the name "generalized information theory" (GIT).Second Edition Intelligent Systems for Engineers and Scientists This book contains information obtained from authentic and highly regarded sources.

Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable Dealing with uncertainty Sources of uncertainty Bayesian.Chapter 9 NON-BAYESIAN FORMALISMS FOR MANAGING UNCERTAINTY Publisher Summary This chapter focuses on Dempster—Shafer (D—S) theory, an alternative method of handling partially specified models.

Rather than completing the model, the - Selection from Probabilistic Reasoning in Intelligent Systems [Book].