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.
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