%{search_type} search results

• Motivation and Outline.- A General Description of Measuring Devices: Plan.- A General Description of Measuring Devices: First Step - Finite Set of Possible Outcomes.- A General Description of Measuring Devices: Second Step - Pairs of Compatible Outcomes.- A General Description of Measuring Devices: Third Step - Subsets of Compatible Outcomes.- A General Description of Measuring Devices: Fourth Step - Conditional Statements about Possible Outcomes.- A General Description of Measuring Devices: Fifth Step - Disjunctive Conditional Statements about the Possible Outcomes.- A General Description of Measuring Devices: Summary.- Physical Quantities: A General Description.- Properties of Physical Quantities.- Future Work.
• (source: Nielsen Book Data)

This book is about methodological aspects of uncertainty propagation in data processing. Uncertainty propagation is an important problem: while computer algorithms efficiently process data related to many aspects of their lives, most of these algorithms implicitly assume that the numbers they process are exact. In reality, these numbers come from measurements, and measurements are never 100% exact. Because of this, it makes no sense to translate 61 kg into pounds and get the result-as computers do-with 13 digit accuracy. In many cases-e.g., in celestial mechanics-the state of a system can be described by a few numbers: the values of the corresponding physical quantities. In such cases, for each of these quantities, we know (at least) the upper bound on the measurement error. This bound is either provided by the manufacturer of the measuring instrument-or is estimated by the user who calibrates this instrument. However, in many other cases, the description of the system is more complex than a few numbers: we need a function to describe a physical field (e.g., electromagnetic field); we need a vector in Hilbert space to describe a quantum state; we need a pseudo-Riemannian space to describe the physical space-time, etc. To describe and process uncertainty in all such cases, this book proposes a general methodology-a methodology that includes intervals as a particular case. The book is recommended to students and researchers interested in challenging aspects of uncertainty analysis and to practitioners who need to handle uncertainty in such unusual situations.
(source: Nielsen Book Data)

• Why Explainable AI? Why Fuzzy Explainable AI? What Is Fuzzy?.- Defuzzification.- Which Fuzzy Techniques?.- So How Can We Design Explainable Fuzzy AI: Ideas.- How to Make Machine Learning Itself More Explainable.- Final Self-Test.
• (source: Nielsen Book Data)

Modern AI techniques -- especially deep learning -- provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI.
(source: Nielsen Book Data)

• Intro
• Contents
• Part I Introduction
• 1 General Introduction
• 2 (Rational) Individual Decision Making: Main Ideas
• References
• 3 (Rational) Group Decision Making: General Formulas and a New Simplified Derivation of These Formulas
• 3.1 (Rational) Group Decision Making: General Formulas
• 3.2 A New (Simplified) Explanation of Nash's Bargaining Solution
• 3.3 Taking Empathy into Account
• References
• 4 How We Can Control Group Decision Making by Modifying the Proposed Options
• 4.1 Formulation of the Problem
• 4.2 Main Idea and the Resulting Explanation

• 4.3 Proof of the Main Result
• References
• Part II How People Actually Make Decisions
• 5 The Fact That We Can Only Have Approximate Estimates Explains Why Buying and Selling Prices are Different
• 5.1 People's Actual Decisions Often Differ from What Decision Theory Recommends
• 5.2 Buying and Selling Prices are Different: A Phenomenon and Its Current Quantitative Explanations
• 5.3 A New (Hopefully, More Adequate) Quantitative Explanation
• References
• 6 The ``No Trade Theorem'' Paradox
• 6.1 ``No Trade Theorem'' and Why It is a Paradox

• 6.2 Analysis of the Problem and the Resulting Explanation of the ``No Trade Theorem'' Paradox
• 6.3 Auxiliary Result: Decision Theory Explains Why Depressed People are More Risk-Averse
• References
• 7 People Make Decisions Based on Clusters Containing Actual Values
• 7.1 Formulation of the Problem
• 7.2 A Possible Geometric Explanation
• 7.3 Auxiliary Observation: How all This is Related to Our Understanding of Directions
• References
• 8 When Revolutions Succeed
• 8.1 Formulation of the Problem
• 8.2 80/20 Rule: Reminder
• 8.3 How These Two Laws Explain the 3.5% Rule
• References

• 9 How People Combine Utility Values
• 9.1 Common Sense Addition
• 9.2 Towards Precise Formulation of the Problem
• 9.3 Hurwicz Optimism-Pessimism Criterion: Reminder
• 9.4 Analysis of the Problem and the Resulting Explanation of Common Sense Addition
• References
• 10 Biased Perception of Time
• 10.1 Formulation of the Problem
• 10.2 How Decision Theory Can Explain the Telescoping Effect
• References
• 11 Biased Perception of Future Time Leads to Non-Optimal Decisions
• References
• 12 People Have Biased Perception of Other People's Utility
• References

• 13 People Select Approximately Optimal Alternatives
• 13.1 People Use Softmax Instead of Optimization
• 13.2 Problem: Need to Generalize Softmax to the Case of Interval Uncertainty
• 13.3 How to Generalize: The Proposed Solution
• References
• 14 People Make Decisions Using Heuristics. I
• 14.1 Formulation of the Problem
• 14.2 Case When We Only Know the Expected Rates of Return ...
• 14.3 Case When We Only Know the Intervals Containing the Actual ...
• References
• 15 People Make Decisions Using Heuristics. II
• 15.1 Formulation of the Problem

This book describes new techniques for making decisions in situations with uncertainty and new applications of decision-making techniques. The main emphasis is on situations when it is difficult to decrease uncertainty. For example, it is very difficult to accurately predict human economic behavior, so in economics, it is very important to take this uncertainty into account when making decisions. Other areas where it is difficult to decrease uncertainty are geosciences and teaching. The book analyzes the general problem of decision making and shows how its results can be applied to economics, geosciences, and teaching. Since all these applications involve computing, the book also shows how these results can be applied to computing, including deep learning and quantum computing. The book is recommended to researchers, practitioners, and students who want to learn more about decision making under uncertaintyand who want to work on remaining challenges.

• Introduction.- What Are the Most Natural and the Most Frequent Transformations.- Which Functions and Which Families of Functions Are Invariant.- What Is the General Relation Between Invariance And Optimality.- General Application: Dynamical Systems.- First Application to Physics: Why Liquids?.- Second Application to Physics: Warping of Our Galaxy.
• (source: Nielsen Book Data)

The book explores a new general approach to selecting-and designing-data processing techniques. Symmetry and invariance ideas behind this algebraic approach have been successful in physics, where many new theories are formulated in symmetry terms. The book explains this approach and expands it to new application areas ranging from engineering, medicine, education to social sciences. In many cases, this approach leads to optimal techniques and optimal solutions. That the same data processing techniques help us better analyze wooden structures, lung dysfunctions, and deep learning algorithms is a good indication that these techniques can be used in many other applications as well. The book is recommended to researchers and practitioners who need to select a data processing technique-or who want to design a new technique when the existing techniques do not work. It is also recommended to students who want to learn the state-of-the-art data processing. .
(source: Nielsen Book Data)

• Vitae of Hung T. Nguyen
• Almost half a century after our encounter with the theory of information proposed by Joseph Kampé de Fériet
• An Enjoyable Research Journey on Uncertainty
• A Bayesian Dilemma
• The Fell compactification of a poset.

Mainly focusing on processing uncertainty, this book presents state-of-the-art techniques and demonstrates their use in applications to econometrics and other areas. Processing uncertainty is essential, considering that computers - which help us understand real-life processes and make better decisions based on that understanding - get their information from measurements or from expert estimates, neither of which is ever 100% accurate. Measurement uncertainty is usually described using probabilistic techniques, while uncertainty in expert estimates is often described using fuzzy techniques. Therefore, it is important to master both techniques for processing data. This book is highly recommended for researchers and students interested in the latest results and challenges in uncertainty, as well as practitioners who want to learn how to use the corresponding state-of-the-art techniques.

This book presents recent research on probabilistic methods in economics, from machine learning to statistical analysis. Economics is a very important - and at the same a very difficult discipline. It is not easy to predict how an economy will evolve or to identify the measures needed to make an economy prosper. One of the main reasons for this is the high level of uncertainty: different difficult-to-predict events can influence the future economic behavior. To make good predictions and reasonable recommendations, this uncertainty has to be taken into account. In the past, most related research results were based on using traditional techniques from probability and statistics, such as p-value-based hypothesis testing. These techniques led to numerous successful applications, but in the last decades, several examples have emerged showing that these techniques often lead to unreliable and inaccurate predictions. It is therefore necessary to come up with new techniques for processing the corresponding uncertainty that go beyond the traditional probabilistic techniques. This book focuses on such techniques, their economic applications and the remaining challenges, presenting both related theoretical developments and their practical applications.
(source: Nielsen Book Data)

• Introduction.- How to Get More Accurate Estimates.- How to Speed Up Computations.- Towards a Better Understandability of Uncertainty-Estimating Algorithms.- How General Can We Go: What Is Computable and What Is Not.- Decision Making Under Uncertainty.- Conclusions.
• (source: Nielsen Book Data)

How can we solve engineering problems while taking into account data characterized by different types of measurement and estimation uncertainty: interval, probabilistic, fuzzy, etc.? This book provides a theoretical basis for arriving at such solutions, as well as case studies demonstrating how these theoretical ideas can be translated into practical applications in the geosciences, pavement engineering, etc. In all these developments, the authors' objectives were to provide accurate estimates of the resulting uncertainty; to offer solutions that require reasonably short computation times; to offer content that is accessible for engineers; and to be sufficiently general - so that readers can use the book for many different problems. The authors also describe how to make decisions under different types of uncertainty. The book offers a valuable resource for all practical engineers interested in better ways of gauging uncertainty, for students eager to learn and apply the new techniques, and for researchers interested in processing heterogeneous uncertainty. .
(source: Nielsen Book Data)

• Introduction.- Towards a More Adequate Description of Uncertainty.- Towards Justification of Heuristic Techniques for Processing Uncertainty.- Towards More Computationally Efficient Techniques for Processing Uncertainty.- Towards Better Ways of Extracting Information About Uncertainty from Data.
• (source: Nielsen Book Data)

On various examples ranging from geosciences to environmental sciences, this book explains how to generate an adequate description of uncertainty, how to justify semiheuristic algorithms for processing uncertainty, and how to make these algorithms more computationally efficient. It explains in what sense the existing approach to uncertainty as a combination of random and systematic components is only an approximation, presents a more adequate three-component model with an additional periodic error component, and explains how uncertainty propagation techniques can be extended to this model. The book provides a justification for a practically efficient heuristic technique (based on fuzzy decision-making). It explains how the computational complexity of uncertainty processing can be reduced. The book also shows how to take into account that in real life, the information about uncertainty is often only partially known, and, on several practical examples, explains how to extract the missing information about uncertainty from the available data.
(source: Nielsen Book Data)

• Introduction: Symmetries and Similarities as a Methodology for Algorithmics of Analysis, Prediction, and Control in Science and Engineering.- Algorithmic Aspects of Real-Life Systems Analysis: Approach Based on Symmetry and Simila
• Algorithmic Aspects of Prediction: An Approach Based on Symmetry and Similarity
• Algorithmic Aspects of Control: Approach Based on Symmetry and Similarity
• Possible Ideas for FutureWork.

This book demonstrates how to describe and analyze a system's behavior and extract the desired prediction and control algorithms from this analysis. A typical prediction is based on observing similar situations in the past, knowing the outcomes of these past situations, and expecting that the future outcome of the current situation will be similar to these past observed outcomes. In mathematical terms, similarity corresponds to symmetry, and similarity of outcomes to invariance. This book shows how symmetries can be used in all classes of algorithmic problems of sciences and engineering: from analysis to prediction to control. Applications cover chemistry, geosciences, intelligent control, neural networks, quantum physics, and thermal physics. Specifically, it is shown how the approach based on symmetry and similarity can be used in the analysis of real-life systems, in the algorithms of prediction, and in the algorithms of control.

• Uncertainty in Measurement: Some Thoughts about its Expressing and Processing.- Why Two Sigma? A Theoretical Justification for an Empirical Measurement Practice.- Fuzzy Linguistic Scales: Definition, Properties and Applications.- A Fuzzy Shape Specification System to Support Design for Aesthetics.- Generating Membership Functions for a Noise Annoyance Model from Experimental Data.- An Exegesis of Data Fusion.- Possibilistic Logic: A Theoretical Framework for Multiple Source Information Fusion.- Automated Adaptive Situation Assessment.- Soft Computing, Real-time Measurement and Information Processing in a Modern Brewery.- The Aggregation of Industrial Performance Information by the Choquet Fuzzy Integral.- Computing Image with an Analog Circuit Inspired by the Outer Retinal Network.- Extending the Decision Accuracy of a Bioinformatics System.- On Fuzzy Controllers Having Radial Basis Transfer Functions.- Evolutionary Scene Recognition and Simultaneous Position/Orientation Detection.- Evolutionary Dynamics Identification of Multi-Link Manipulators Using Runge - Kutta - Gill RBF Networks.- Towards Reliable Sub-Division of Geological Areas: Interval Approach.- A Fuzzy Classifier with Pyramidal Membership Functions.- A Comparison of Soft Computing and Traditional Approaches for Risk Classification and Claim Cost Prediction in the Automobile Insurance Industry.- Evolutionary Rule Generation and its Application to Credit Scoring.- Social Fuzziology in Action: Acquisition and Making Sense of Social Information.
• (source: Nielsen Book Data)

This volume covers the fields of measurement and information acqulSltlon. It contains a collection of papers representing the current research trends in these areas. What are those trends? The first one is the enormous growth in the amount of information and the amazing technologies, which make this information available anywhere and anytime. The second one is a substantial development of methods of the information presentation including, to name just a few, multimedia, virtual environment, computer animation. The third one is the all-time boosting demand for improving the quality of decisions made on the base of this information in various applications ranging from engineering to business. Nowadays information acquisition should not only provide more information but also provide it in such a way as to assure effective and efficient processing of this information. And here comes a relatively new methodology of soft computing. Application of soft computing in measurement and information acquisition is considered in this volume.
(source: Nielsen Book Data)

• 1. Informal Introduction: Data Processing, Interval Computations, and Computational Complexity. 2. The Notions of Feasibility and NP-Hardness: Brief Introduction. 3. In the General Case, The Basic Problem of Interval Computations is Intractable. 4. Basic Problem of Interval Computations for Polynomials of a Fixed Number of Variables. 5. Basic Problem of Interval Computations for Polynomials of Fixed Order. 6. Basic Problem of Interval Computations for Polynomials with Bounded Coefficients. 7. Fixed Data Processing Algorithms, Varying Data: Still NP-Hard. 8. Fixed Data, Varying Data Processing Algorithms: Still Intractable. 9. What if we Only Allow Some Arithmetic Operations in Data Processing? 10. For Fractionally-Linear Functions, A Feasible Algorithm Solves the Basic Problem of Interval Computations. 11. Solving Interval Linear Systems is NP-Hard. 12. Interval Linear Systems: Search for Feasible Classes. 13. Physical Corollary: Prediction is Not Always Possible, Even for Linear Systems with Known Dynamics. 14. Engineering Corollary: Signal Processing is NP-Hard. 15. Bright Sides of NP-Hardness of Interval Computations I: NP-Hard Means that Good Interval Heuristics Can Solve Other Hard Problems. 16. If Input Intervals are Narrow Enough, then Interval Computations are Almost Always Easy. 17. Optimization
• A First Example of a Numerical Problem in Which Interval Methods are Used: Computational Complexity and Feasibility. 18. Solving Systems of Equations. 19. Approximation of Interval Functions. 20. Solving Differential Equations. 21. Properties of Interval Matrices I: Main Results. 22. Properties of Interval Matrices II: Proofs and Auxiliary Results. 23. Non-Interval Uncertainty I: Ellipsoid Uncertainty And its Generalizations. 24. Non-Interval Uncertainty II: Multi-Intervals and Their Generalizations. 25. What if Quantities are Discrete? 26. Error Estimation for Indirect Measurements: Interval Computation Problem is (Slightly) Harder than a Similar Probabilistic Computational Problem. A: In Case of Interval (or More General) Uncertainty, No Algorithm can Choose the Simplest Representative. B: Error Estimation for Indirect Measurements: Case of Approximately Known Functions. C: From Interval Computations to Modal Mathematics. D: Beyond NP: Two Roots Good, One Root Better. E: Does `NP-Hard' Really Mean `Intractable'? F: Bright Sides of NP-Hardness of Interval Computations II: Freedom of Will? G: The Worse, the Better: Paradoxical Computational Complexity of Interval Computations and Data Processing.
• (source: Nielsen Book Data)

The input data for data processing algorithms come from measurements and are hence not precise. We therefore need to estimate the accuracy of the results of data processing. It turns out that even for the simplest data processing algorithms, this problem is, in general, intractable. This book describes for what classes of problems interval computations (i.e. data processing with automatic results verification) are feasible, and when they are intractable. This knowledge is important, e.g. for algorithm developers, because it will enable them to concentrate on the classes of problems for which general algorithms are possible.
(source: Nielsen Book Data)

• Algorithm Complexity: Two Simple Examples.
• 2. Solving General Linear Functional Equations: An Application to Algorithm Complexity.
• 3. Program Testing: A Problem.
• 4. Optimal Program Testing.
• 5. Optimal Choice of a Penalty Function: Simplest Case of Algorithm Design.
• 6. Solving General Linear Differential Equations with Constant Coefficients: An Application to Constrained Optimization.
• 7. Simulated Annealing: `Smooth' (Local) Discrete Optimization.
• 8. Genetic Algorithms: `Non-Smooth' Discrete Optimization.
• 9. RISC Computer Architecture and Internet Growth: Two Applications of Extrapolation.
• 10. Systems of Differential Equations and Their Use in Computer-Related Extrapolation Problems.
• 11. Network Congestion: An Example of Non-Linear Extrapolation.
• 12. Neural Networks: A General Form of Non-Linear Extrapolation.
• 13. Expert Systems and the Basics of Fuzzy Logic.
• 14. Intelligent and Fuzzy Control.
• 15. Randomness, Chaos, and Fractals. A: Simulated Annealing Revisited. B: Software Cost Estimation. C: Electronic Engineering: How to Describe PN-Junctions. D: Log-Normal Distribution Justified: An Application to Computational Statistics. E: Optimal Robust Statistical Methods. F: How to Avoid Paralysis of Neural Networks. G: Estimating Computer Prices. H: Allocating Bandwidth on Computer Networks. I: Algorithm Complexity Revisited. J: How Can a Robot Avoid Obstacles: Case Study of Real-Time Optimization. K: Discounting in Robot Control: A Case Study of Dynamic Optimization. Index.
• (source: Nielsen Book Data)

This volume is intended to be used as a textbook for a special topic course in computer science. It addresses contemporary research topics of interest such as intelligent control, genetic algorithms, neural networks, optimization techniques, expert systems, fractals, and computer vision. The work incorporates many new research ideas, and focuses on the role of continuous mathematics. This book will be valuable to graduate students interested in theoretical computer topics, algorithms, expert systems, neural networks, and software engineering.
(source: Nielsen Book Data)