Springer Computational Intelligence in Generative Artificial Intelligence: Uncertainty Treatment and Decision-Making (Studies in Computational Intelligence, 1293)

This book is aimed at all those interested in the combined application of computational intelligence techniques and Generative Artificial Intelligence for Uncertainty Treatment and Decision Making. The book is organized into three parts. The first part groups work related to new algorithms and techniques for handling uncertainty in generative artificial intelligence models. It also addresses topics associated with regulatory models for the development of Artificial Intelligence. The second part is dedicated to articles related to architectures of agentic artificial intelligence solutions. The third part includes works dedicated to supporting decision-making under uncertainty in business and engineering environments. This book combines different artificial intelligence techniques for solving decision-making problems, among which the following stand out: generative artificial intelligence, linguistic data summarization techniques, neutrosphophic theory, word computing, among other techniques. The techniques proposed in the book aim to simulate human tolerance in decision-making processes in environments with uncertainty and imprecision. The authors of the book stand out for their extensive experience in the development of basic and applied applications of computational intelligence. The authors Pedro Y. Piñero Pérez, Iliana Pérez Pupo, Janusz Kacprzyk and Rafael E. Bello Pérez have published several books associated with artificial intelligence and applied computational intelligence. They continue to work on fundamental and applied research on different artificial intelligence techniques to assist decision-making in different areas of knowledge.