Artificial Intelligence with Uncertainty

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.

Deyi Li earned his Ph.D. in computer science at Heriot-Watt University in Edinburgh, United Kingdom in 1983. He was elected as a member of the Chinese Academy of Engineering in 1999 and a member of the Eurasian Academy of Engineering in 1999 and a member of the Eruasian Academy of Sciences in 2004. At present, he is a professor at Tsinghua University and vice president of the Chinese Institute of Electronics and the Chinese Association of Artificial Intelligence. He has published more than 100 papers on a wide range of topics in artificial intelligence.

Yi Du earned his Ph.D. in computer science at PLA University of Science and Technology in Nanjing, China in 2000. He received his undergraduate degree from Nanjing Institute of Communication Engineering in 1993. At present, he is a senior engineer in a network management center in Beijing.