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Complex adaptive systems science meets systems engineering
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Advances in Robotics & Automation

ISSN: 2168-9695

Open Access

Complex adaptive systems science meets systems engineering


World Congress on Industrial Automation

July 20-22, 2015 San Francisco, USA

Margery J Doyle

Posters-Accepted Abstracts: Adv Robot Autom

Abstract :

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.

Google Scholar citation report
Citations: 1004

Advances in Robotics & Automation received 1004 citations as per Google Scholar report

Advances in Robotics & Automation peer review process verified at publons

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