Margery J Doyle
Posters-Accepted Abstracts: Adv Robot Autom
Recently in an effort to not only improve military training CGFs but to also establish a capability to rapidly develop agentbased models for multitude of uses to support the warfighter, the Air Force Research Lab 711/HPW Warfighter Readiness Research Division, along with a few partners from industry, developed, facilitated and promoted a ├ó┬?┬?Not-So-Grand-Challenge├ó┬?┬Ł (NSGC) effort. In this article we report the methods employed to develop, integrate, and test complex adaptive agent-based models in a complex training research environment, the use-case as applied. To do so, we capitalized on the properties of complex adaptive situations, context-based agent-based modeling, and the utility of modularization and/or decomposition of an agent or system├ó┬?┬?s functional properties. We found that development and use of Knowledge-to-Model (k2Mod) Environment Abstraction (EA) architecture gives agents the capacity to recognize gain situation awareness, recognize change in their environment, and react appropriately. This method also facilitates the speed by which new agent definitions can be developed. In addition, formalizing such a protocol affords the Modeling and Simulation community a process that promotes portability, usability, reusability and composability for rapid agent-based modeling development in complex environments.
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