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Deep Neural Network Based Agents: The Artificially Intelligent Deeply Learned from Historical Battle Data and Sun Tzu's The Art of War Autonomous Forces on the March

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Description

Can Autonomous Forces be trained with artificial intelligence (AI) algorithms like deep learning from historical data and further be improved by the rules for developing a state-of-the-art system of planning and analysis of Military Strategy? The application of Machine learning (ML) algorithms is general practice for extracting meaningful models from the data. ML could be unsupervised, supervised, or reinforcement learning. This book explores deep learning neural networks for extracting model parameters for generating autonomous forces, Although our main focus is on the world's largest tank battle the Battle of Kursk, we are also exploring other historical battle data of the world for validating the system. The battle of Kursk between the Soviets and Germans is known to be the biggest tank battle in history. This book explores the two-dimensional-simplex tank and artillery data from the Kursk database for analyzing a class of discrete-time homogeneous and heterogeneous Lanchester models. Under homogeneous form, the Soviet’s (or German’s) tank casualty is attributed to only the German’s (or Soviet’s) tank engagement. The tank casualty is attributed to both tank and artillery engagements for heterogeneous form. For validating the models, different goodness-of-fit statistics are used for comparison.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
18 May 2024
Listed Since
24 June 2024

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