Description:
Uncertainty in Computational Intelligence-Based Decision-Making focuses on techniques for reasoning and decision-making under uncertainty that are used to solve issues in artificial intelligence (AI). It covers a wide range of subjects, including knowledge acquisition and automated model construction, pattern recognition, machine learning, natural language processing, decision analysis, and decision support systems, among others.
The first chapter of this book provides a thorough introduction to the topics of causation in Bayesian belief networks, applications of uncertainty, automated model construction and learning, graphic models for inference and decision making, and qualitative reasoning. The following chapters examine the fundamental models of computational techniques, computational modeling of biological and natural intelligent systems, including swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems, and evolutionary computation. They also examine decision making and analysis, expert systems, and robotics in the context of artificial intelligence and computer science.Brief description: Dumitru Baleanu is a full professor at the Institute of Space Sciences, Romania. Fractional dynamics and its applications, fractional differential equations, discrete mathematics, dynamic systems on time scales, the wavelet method and its applications, quantization of the systems with constraints, the Hamilton-Jacobi formalism, and geometries admitting generic and non-generic symmetries are among Dumitru's research interests. He has edited five books and published more than 1400 papers. He serves on several journals' editorial boards. With more than 30000 citations, Dumitru has served as a referee for more than 200 journals. He was included in the 2015-2020 Thompson Reuter list of the top 1% of highly cited researchers.