Buch, Englisch, 375 Seiten, Format (B × H): 155 mm x 235 mm
Uncertainty Treatment and Decision-Making
Buch, Englisch, 375 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Studies in Computational Intelligence
ISBN: 978-3-032-29287-2
Verlag: Springer Nature Switzerland AG
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
.- Modeling and Handling Uncertainty in Generative Artificial Intelligence.
.- Combining OWA Neutrosophic Operators and LogProbs for Uncertainty Evaluation in Agentic AI Outputs.
.- Evaluation of Plausibility and Robustness in Counterfactual Explanations.
.- Regulatory Model and Best Practices for the Development of Artificial Intelligence in Contexts of Uncertainty.
.- Uncertainty: another aspect to consider in XAI.
.- Architectures and Solutions Based on Intelligent Agents.
.- Multilayer Memory Architecture for Multi-agent Intelligent Decision-Making in uncertainty environment.
.- Layered hybrid architecture for uncertainty mitigation in virtual assistants.
.- Prototype of an Autonomous System for Integrated Cybersecurity Management.
.- Algorithms and Patterns for Building Agent-Based Artificial Intelligence Solutions assisted by Soft Computing Techniques.
.- Artificial Intelligence for Advanced Data Analysis and Decision-Making under Uncertainty.
.- Ecosystem for Decision-Making in High-Performance Athletes Combining Generative AI and Soft Computing Under Uncertainty.
.- Generative AI Enhanced Predictive Prescriptive Analytics for Uncertainty Aware Decision Making: Talent Management Case Study.
.- Probabilistic Tree-Based Approach for Linguistic Data Summarization and Knowledge Generation under Uncertainty in Sustainability Analysis.
.- Computational Intelligence and Generative Artificial Intelligence for Legal Claims Management Under Uncertainty.




