[PDF] Uncertainty In Artificial Intelligence 2 eBook
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This second volume is arranged in four sections: Analysis contains papers which compare the attributes of various approaches to uncertainty. Tools provides sufficient information for the reader to implement uncertainty calculations. Papers in the Theory section explain various approaches to uncertainty. The Applications section describes the difficulties involved in, and the results produced by, incorporating uncertainty into actual systems.
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.
"This book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory. The main purpose is to equip the readers with an axiomatic approach to deal with uncertainty. Mathematicians, researchers, engineers, designers and students in the field of applied mathematics, operations research, statistics, industrial engineering, information science and management science will find this work a useful reference."--BOOK JACKET. Title Summary field provided by Blackwell North America, Inc. All Rights Reserved.
Learning with uncertainty covers a broad range of scenarios in machine learning, this book mainly focuses on: (1) Decision tree learning with uncertainty, (2) Clustering under uncertainty environment, (3) Active learning based on uncertainty criterion, and (4) Ensemble learning in a framework of uncertainty. The book starts with the introduction to uncertainty including randomness, roughness, fuzziness and non-specificity and then comprehensively discusses a number of key issues in learning with uncertainty, such as uncertainty representation in learning, the influence of uncertainty on the performance of learning system, the heuristic design with uncertainty, etc. Most contents of the book are our research results in recent decades. The purpose of this book is to help the readers to understand the impact of uncertainty on learning processes. It comes with many examples to facilitate understanding. The book can be used as reference book or textbook for researcher fellows, senior undergraduates and postgraduates majored in computer science and technology, applied mathematics, automation, electrical engineering, etc.
6.3.1.3 Initial Distribution and Presentation of Individual Behavior -- 6.3.2 Data Field to Reflect Mutual Spread of Applause -- 6.3.3 Computing Model for "Applause Sounded"--6.3.4 Experimental Platform -- 6.3.5 Diversity Analysis of Emergence -- 6.3.6 Guided Applause Synchronization -- References -- Chapter 7: Great Development of Artificial Intelligence with Uncertainty due to Cloud Computing -- 7.1 An Insight into the Contributions and Limitations of Fuzzy Set from the Perspective of a Cloud Model -- 7.1.1 Paradoxical Argument over Fuzzy Logic -- 7.1.2 Dependence of Fuzziness on Randomness -- 7.1.3 From Fuzzy to Uncertainty Reasoning -- 7.2 From Turing Computing to Cloud Computing -- 7.2.1 Cloud Computing beyond the Turing Machine -- 7.2.2 Cloud Computing and Cloud Model -- 7.2.3 Cloud Model Walking between Gaussian and Power Law Distribution -- 7.3 Big Data Calls for AI with Uncertainties -- 7.3.1 From Database to Big Data -- 7.3.2 Network Interaction and Swarm Intelligence -- 7.4 Prospect of AI with Uncertainty -- References -- Index
Formal ways of representing uncertainty and various logics for reasoning about it; updated with new material on weighted probability measures, complexity-theoretic considerations, and other topics. In order to deal with uncertainty intelligently, we need to be able to represent it and reason about it. In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it. While the ideas presented are formalized in terms of definitions and theorems, the emphasis is on the philosophy of representing and reasoning about uncertainty. Halpern surveys possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures; considers the updating of beliefs based on changing information and the relation to Bayes' theorem; and discusses qualitative, quantitative, and plausibilistic Bayesian networks. This second edition has been updated to reflect Halpern's recent research. New material includes a consideration of weighted probability measures and how they can be used in decision making; analyses of the Doomsday argument and the Sleeping Beauty problem; modeling games with imperfect recall using the runs-and-systems approach; a discussion of complexity-theoretic considerations; the application of first-order conditional logic to security. Reasoning about Uncertainty is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.
Hardbound. How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.