Artificial Intelligence with Uncertainty

Artificial Intelligence with Uncertainty

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The information deluge currently assaulting us in the 21st century is having a profound impact on our lifestyles and how we work. We must constantly separate trustworthy and required information from the massive amount of data we encounter each day. Through mathematical theories, models, and experimental computations, Artificial Intelligence with Uncertainty explores the uncertainties of knowledge and intelligence that occur during the cognitive processes of human beings. The authors focus on the importance of natural language-the carrier of knowledge and intelligence-for artificial intelligence (AI) study. This book develops a framework that shows how uncertainty in AI expands and generalizes traditional AI. It describes the cloud model, its uncertainties of randomness and fuzziness, and the correlation between them. The book also centers on other physical methods for data mining, such as the data field and knowledge discovery state space. In addition, it presents an inverted pendulum example to discuss reasoning and control with uncertain knowledge as well as provides a cognitive physics model to visualize human thinking with hierarchy.
With in-depth discussions on the fundamentals, methodologies, and uncertainties in AI, this book explains and simulates human thinking, leading to a better understanding of cognitive processes.
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Product details

  • Hardback | 376 pages
  • 160.02 x 238.76 x 25.4mm | 589.67g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • New.
  • 196 black & white illustrations, 20 black & white tables, 10 black & white halftones
  • 1584889985
  • 9781584889984

Table of contents

PREFACE THE 50-YEAR HISTORY OF ARTIFICIAL INTELLIGENCE Departure from Dartmouth Symposium Expected Goals as Time Goes on AI Achievements in 50 years Major Development of AI in the Information Age The Cross Trend between AI, Brain Science, and Cognitive Science METHODOLOGIES OF AI Symbolism Methodology Connectionism Methodology Behaviorism Methodology Reflection on Methodologies ON UNCERTAINTIES OF KNOWLEDGE On Randomness On Fuzziness Uncertainties in Natural Languages Uncertainties in Commonsense Knowledge Other Uncertainties of Knowledge MATHEMATICAL FOUNDATION OF AI WITH UNCERTAINTY Probability Theory Fuzzy Set Theory Rough Set Theory Chaos and Fractal Kernel Functions and Principal Curves QUALITATIVE AND QUANTITATIVE TRANSFORM MODEL-CLOUD MODEL Perspectives in the Study of AI with Uncertainty Representing Concepts Using Cloud Models Normal Cloud Generator Mathematical Properties of Normal Cloud On the Pervasiveness of the Normal Cloud Model DISCOVERING KNOWLEDGE WITH UNCERTAINTY THROUGH METHODOLOGIES IN PHYSICS From Perception of Physical World to Perception of Human Self Data Field Uncertainty in Concept Hierarchy Knowledge Discovery State Space DATA MINING FOR DISCOVERING KNOWLEDGE WITH UNCERTAINTY Uncertainty in Data Mining Classification and Clustering with Uncertainty Discovery of Association Rules with Uncertainty Time Series Data Mining and Forecasting REASONING AND CONTROL OF QUALITATIVE KNOWLEDGE Qualitative Rule Construction by Cloud Qualitative Control Mechanism Inverted Pendulum: An Example of Intelligent Control with Uncertainty A NEW DIRECTION OF AI WITH UNCERTAINTY Computing with Words Study on Cognitive Physics Complex Networks with Small World and Scale-Free Models Long Way to Go for AI with Uncertainty INDEX
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About Deyi Li

Tsinghua University, Beijing, China Network Management Center, Beijing, China
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