Description
Book SynopsisThe 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.
Trade Review"There are many good examples included in the book . . . clearly written from an AI and computer science perspective."
– Thomas Studer, in Zentralblatt Math, 2009
Table of ContentsPreface. The 50-Year History of Artificial Intelligence. Methodologies of AI. On Uncertainties of Knowledge. Mathematical Foundation of AI with Uncertainty. Qualitative and Quantitative Transform Model-Cloud Model. Discovering Knowledge with Uncertainty through Methodologies in Physics. Data Mining for Discovering Knowledge with Uncertainty. Reasoning and Control of Qualitative Knowledge. A New Direction of AI with Uncertainty. Index.
