Computational Decision Intelligence under Uncertainty

Uncertainty, Computational Techniques, and Decision Intelligence explores basic concepts and focuses on methods of reasoning and decision making under uncertainty which are applied to problems in artificial intelligence (AI), including issues of knowledge acquisition and automated model construction, pattern recognition, machine learning, NLP, decision analysis, and decision support systems. Comprised of three sections, the book begins with a comprehensive introduction to...
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Uncertainty, Computational Techniques, and Decision Intelligence explores basic concepts and focuses on methods of reasoning and decision making under uncertainty which are applied to problems in artificial intelligence (AI), including issues of knowledge acquisition and automated model construction, pattern recognition, machine learning, NLP, decision analysis, and decision support systems. Comprised of three sections, the book begins with a comprehensive introduction to causality in Bayesian belief networks, applications of uncertainty, automated model construction and learning, graphical models for inference and decision making, and qualitative reasoning. Subsequent chapters comprehensively explore a range of basic models of computational techniques and computational modelling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems, evolutionary computation, decision making and analysis, and expert systems & robotics. The book provides readers with a deep dive into Uncertainty encountered in artificial intelligence and computational intelligence paradigms and algorithms; inviting readers to implement and problem solve real-world, complex problems within the CI development framework, decision support systems, and visual decision design.

• Presents a　set of tools which researchers and engineers can use　to model and process uncertain knowledge about an environment and specific applications
• Explores how methods from the field of decision intelligence, like descriptive, diagnostic, and predictive analytics, can be used to improve computational modelling
• Explains how computation and decision intelligence can be applied to the management of uncertainty, inconsistency, vagueness, and preferences, tackling the problems of dynamic, real-world scenarios