AI Bibliography
AI Bibliography |
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Blasch, E., Schuck, T., & Gagne, O. B. (2021). Trusted entropy-based information maneuverability for ai information systems engineering. In Engineering Artificially Intelligent Systems (pp. 34–52). Springer. |
| Resource type: Book Article BibTeX citation key: Blasch2021 View all bibliographic details  |
Categories: Artificial Intelligence, Biological Science, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, Engineering, General, Mathematics, Military Science Subcategories: Analytics, Big data, Chaos theory, Cognitive Electronic Warfare, Command and control, Cyber, Decision making, Edge AI, Forecasting, Game theory, JADC2, Machine learning, Military research, Networked forces, Strategy, Systems theory Creators: Blasch, Gagne, Schuck Publisher: Springer Collection: Engineering Artificially Intelligent Systems |
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Future battle conflicts will be fought in information space as opposed to a classical physical space. The purpose of the Chapter highlights the overlap of Electromagnetic Maneuver Warfare (EMW) and cyber warfare (CW) concepts with the measures of information power and information maneuverability. Specifically, the Chapter utilizes Col. John Boyd’s Observe-Orient-Decide-Act (OODA) process and his “Destruction and Creation” thesis where he originated “Energy-Maneuverability” (EM) theory towards information power and information maneuverability. With the proliferation of EMW/CW, there is a need to transition from physical entropy and specific energy to proposed information equivalents. The entropy approach to energy analysis is consistent with robust artificial intelligence (AI)/machine learning (ML) methods. Introducing the concepts of “strong” and “weak” information positions as compared to an adversary or competitor – hence “information maneuverability” (IM), the Chapter lists the theoretical background to develop IM. Decision speed is a hallmark of superior agility and autonomy systems within the systems engineering information domain. Results demonstrate from force ratio use cases the importance of trusted information, achieving a five-fold increase on force structure analytics.
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