Titlebook: Computational Red Teaming; Risk Analytics of Bi Hussein A. Abbass Book 2015 Springer International Publishing Switzerland 2015 Architecture

小争吵

critique

粗糙滥制

Big-Data-to-Decisions Red Teaming Systems,nd big data before presenting the intelligent architectures that can transform data to decisions in CRT systems. Most of these architectures can be used outside CRT in any situation. However, augmenting these architectures with CRT capabilities offers unprecedented computational capabilities for off

贝雷帽

Thinking Tools for Computational Red Teaming,tic manner that is easy to manage. Scenarios as a mean to capture complex plausible futures are discussed, followed by the presentation of guidelines on designing scenarios for CRT exercises. Organizations are complex entities. As such, one section is dedicated to discussing the complexity resulting

Wordlist

Case Studies on Computational Red Teaming,ed, where CRT is used to evaluate three conflict-detection algorithms in the domain of air-traffic management (ATM). The second case study presents a lab-based experiment using a game between humans and computers. The objective of the experiment is to test the impact of noise in the information pres

十字架

The Way Forward,k on three principles to describe a science: simplicity, coherence, and utility. In writing this book, these three principles were closely followed. The language has been simplified to bridge the gap between management and computational scientists; architectures were used to both connect the concept

利用

nonsensical

起草

Obsequious

Book 2015ntation, optimization, simulation, data mining, big data, cognitive information processing, and system thinking are blended together systematically to present CRT as the science of Risk Analytics and Challenge Analytics. Utility: where the author draws on a wide range of examples, ranging from job i