We describe an approach for dynamically generating asymmetric tactics that can drive adversary behaviors in synthetic training environments. GAMBIT (Genetically Actualized Models of Behavior for Insurgent Tactics) features a genetic algorithm and tactic evaluation engine that – provided a computational specification of a domain and notional representation of the trainee’s tactics – will automatically generate a tactic that will be effective given those inputs. That tactic can then be executed using embedded behavior models within a virtual or constructive simulation. GAMBIT-generated tactics can evolve across training exercises by modifying the representation of the trainee’s tactics in response to his observed behavior.

Reference:

Stensrud, B., Reece, D., Piegdon, N. and Wu, A. (2008), “Asymmetric Adversary Tactics for Synthetic Training Environments,” Proceedings of the 17th Conference on Behavior Representation in Modeling and Simulation (BRIMS) Conference, Providence, RI, April 14-17, 2008

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