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Conflict Decision Method based on Quadratic Combination

Author : Xin Guan

Publisher : Infinite Study

Page : 16 pages

File Size : 24,26 MB

Release :

Category : Mathematics

ISBN :

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Book Description

There are many unsatisfactory situations in the existing improvement methods of evidence theory, such as a large amount of calculation, the normalization process is unreasonable, the evidence combination effect is not ideal in the conflict evidence decision-making process, and so on. This paper proposes a method based on quadratic combination of conflict evidence to improve the above situations. Firstly, a new flow chart of conflict evidence decision method based on quadratic combination is proposed. Secondly, a new multiplicative normalization rule is proposed, and the new rule is analyzed to verify its rationality. Thirdly, the shortcomings of the existing conflict measurement methods are analyzed, a new conflict measurement function is proposed, and the rationality of the new function is analyzed. Finally, through the analysis of the example and comparison with the existing evidence combination rules, the effectiveness of the method of this paper is verified.



Multiple Criteria Decision Making

Author : Y. Ilker Topcu

Publisher : Springer Nature

Page : 413 pages

File Size : 15,68 MB

Release : 2021-03-24

Category : Business & Economics

ISBN : 303052406X

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Book Description

Data and its processed state 'information' have become an indispensable resource for virtually all aspects of business, education, etc. Consequently, decisions regarding the handling of this data, transforming it into meaningful information, and ultimately arriving at the best course of action have taken on a new importance. This book highlights a selection of cutting-edge research on decision making presented at the 25th International Conference on Multiple Criteria Decision Making (MCDM 2019), held in Istanbul, Turkey.



A novel decision probability transformation method based on belief interval

Author : Zhan Deng

Publisher : Infinite Study

Page : 11 pages

File Size : 15,78 MB

Release :

Category : Education

ISBN :

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Book Description

In Dempster–Shafer evidence theory, the basic probability assignment (BPA) can effectively represent and process uncertain information. How to transform the BPA of uncertain information into a decision probability remains a problem to be solved. In the light of this issue, we develop a novel decision probability transformation method to realize the transition from the belief decision to the probability decision in the framework of Dempster–Shafer evidence theory. The newly proposed method considers the transformation of BPA with multi-subset focal elements from the perspective of the belief interval, and applies the continuous interval argument ordered weighted average operator to quantify the data information contained in the belief interval for each singleton. Afterward, we present an approach to calculate the support degree of the singleton based on quantitative data information. According to the support degree of the singleton, the BPA of multi-subset focal elements is allocated reasonably. Furthermore, we introduce the concepts of probabilistic information content in this paper, which is utilized to evaluate the performance of the decision probability transformation method. Eventually, a few numerical examples and a practical application are given to demonstrate the rationality and accuracy of our proposed method.



On the belief universal gravitation (BUG)

Author : Xiangjun Mi

Publisher : Infinite Study

Page : 30 pages

File Size : 28,79 MB

Release :

Category : Mathematics

ISBN :

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Book Description

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.



Advances in Machine Learning and Cybernetics

Author : Daniel S. Yeung

Publisher : Springer

Page : 1129 pages

File Size : 35,21 MB

Release : 2006-05-05

Category : Computers

ISBN : 3540335854

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Book Description

This book constitutes the thoroughly refereed post-proceedings of the 4th International Conference on Machine Learning and Cybernetics, ICMLC 2005, held in Guangzhou, China in August 2005. The 114 revised full papers of this volume are organized in topical sections on agents and distributed artificial intelligence, control, data mining and knowledge discovery, fuzzy information processing, learning and reasoning, machine learning applications, neural networks and statistical learning methods, pattern recognition, vision and image processing.



Operations Research Proceedings 2003

Author : Dino Ahr

Publisher : Springer Science & Business Media

Page : 516 pages

File Size : 47,98 MB

Release : 2004-05-05

Category : Business & Economics

ISBN : 9783540214458

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Book Description

This volume contains a selection of papers referring to lectures presented at the symposium "Operations Research 2003" (OR03) held at the Ruprecht Karls-Universitiit Heidelberg, September 3 - 5, 2003. This international con ference took place under the auspices of the German Operations Research So ciety (GOR) and of Dr. Erwin Teufel, prime minister of Baden-Wurttemberg. The symposium had about 500 participants from countries all over the world. It attracted academians and practitioners working in various field of Opera tions Research and provided them with the most recent advances in Opera tions Research and related areas in Economics, Mathematics, and Computer Science. The program consisted of 4 plenary and 13 semi-plenary talks and more than 300 contributed papers selected by the program committee to be presented in 17 sections. Due to a limited number of pages available for the proceedings volume, the length of each article as well as the total number of accepted contributions had to be restricted. Submitted manuscripts have therefore been reviewed and 62 of them have been selected for publication. This refereeing procedure has been strongly supported by the section chairmen and we would like to express our gratitude to them. Finally, we also would like to thank Dr. Werner Muller from Springer-Verlag for his support in publishing this proceedings volume.



Information Fusion Under Consideration of Conflicting Input Signals

Author : Uwe Mönks

Publisher : Springer

Page : 249 pages

File Size : 31,59 MB

Release : 2016-11-25

Category : Technology & Engineering

ISBN : 3662537524

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Book Description

This work proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) and the μBalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. In addition, a sensor defect detection method, which is based on the continuous monitoring of sensor reliabilities, is presented. The performances of the contributions are shown by their evaluation in the scope of both a publicly available data set and a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms.



Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions

Author : Sucar, L. Enrique

Publisher : IGI Global

Page : 444 pages

File Size : 13,51 MB

Release : 2011-10-31

Category : Computers

ISBN : 160960167X

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Book Description

One of the goals of artificial intelligence (AI) is creating autonomous agents that must make decisions based on uncertain and incomplete information. The goal is to design rational agents that must take the best action given the information available and their goals. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions provides an introduction to different types of decision theory techniques, including MDPs, POMDPs, Influence Diagrams, and Reinforcement Learning, and illustrates their application in artificial intelligence. This book provides insights into the advantages and challenges of using decision theory models for developing intelligent systems.



Decision Procedures

Author : Daniel Kroening

Publisher : Springer Science & Business Media

Page : 314 pages

File Size : 31,36 MB

Release : 2008-04-29

Category : Computers

ISBN : 3540741054

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Book Description

A decision procedure is an algorithm that, given a decision problem, terminates with a correct yes/no answer. Here, the authors focus on theories that are expressive enough to model real problems, but are still decidable. Specifically, the book concentrates on decision procedures for first-order theories that are commonly used in automated verification and reasoning, theorem-proving, compiler optimization and operations research. The techniques described in the book draw from fields such as graph theory and logic, and are routinely used in industry. The authors introduce the basic terminology of satisfiability modulo theories and then, in separate chapters, study decision procedures for each of the following theories: propositional logic; equalities and uninterpreted functions; linear arithmetic; bit vectors; arrays; pointer logic; and quantified formulas.



Bayesian Models of Perception and Action

Author : Wei Ji Ma

Publisher : MIT Press

Page : 409 pages

File Size : 42,94 MB

Release : 2023-08-08

Category : Science

ISBN : 0262047594

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Book Description

An accessible introduction to constructing and interpreting Bayesian models of perceptual decision-making and action. Many forms of perception and action can be mathematically modeled as probabilistic—or Bayesian—inference, a method used to draw conclusions from uncertain evidence. According to these models, the human mind behaves like a capable data scientist or crime scene investigator when dealing with noisy and ambiguous data. This textbook provides an approachable introduction to constructing and reasoning with probabilistic models of perceptual decision-making and action. Featuring extensive examples and illustrations, Bayesian Models of Perception and Action is the first textbook to teach this widely used computational framework to beginners. Introduces Bayesian models of perception and action, which are central to cognitive science and neuroscience Beginner-friendly pedagogy includes intuitive examples, daily life illustrations, and gradual progression of complex concepts Broad appeal for students across psychology, neuroscience, cognitive science, linguistics, and mathematics Written by leaders in the field of computational approaches to mind and brain