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Decisionmaking within the Future Battle Command structure will demand an increasing ability to comprehend and structure information on the battlefield. As the military evolves into a networked force, headquarters and others must collect and utilize information from across the battlefield in a timely and efficient manner. Decision aids and solution methodologies in constructive simulations must be modified to better show how this information affects decisions. Using information about friendly and enemy forces and the terrain, a RAND-developed model that incorporates a genetic algorithm (1) determines preferred Blue routes around Red forces and (2) allocates forces to these routes. This model is unique in its incorporation of many higher-level intelligence products — including intelligence about Red's location, activity, intent, military capability, intelligence capability, and adaptability — into the planning algorithm. The integration of these products allows the model to produce sophisticated look-ahead representations of enemy forces that are superior to the static representations typically used in planning sessions. The model also features terrain representations that measure impassibility, inhospitableness, and shadowing, allowing planners to transcend scenarios that assume a lack of interesting terrain.
Table of Contents
Chapter One
Introduction
Chapter Two
Modeling Enemy Capability and Effects
Chapter Three
Generating Blue AoAs: The Phase One Genetic Algorithm
Chapter Four
Generating Blue Allocations: The Phase Two Genetic Algorithm
Chapter Five
Modeling Terrain
Chapter Six
Proof-of-Principle Examples
Chapter Seven
Conclusions and Future Extensions
Research conducted by
The research described in this report was sponsored by the United States Army and conducted by RAND Arroyo Center.
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