Title

Asymmetric Adversary Tactics For Synthetic Training Environments

Abstract

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.

Publication Date

12-1-2008

Publication Title

2008 BRIMS Conference - Behavior Representation in Modeling and Simulation

Number of Pages

155-164

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84865341713 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84865341713

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