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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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