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.
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
"There are many good examples included in the book . . . clearly written from an AI and computer science perspective."
– Thomas Studer, in Zentralblatt Math, 2009
