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Genetic Algorithm Applied to Multi-Agent War Gaming Simulation
| Format: | Interactive/Lecture |
| Facilitator(s): | Mark Rivera, Boeing Company James Mullins, Phoenix Integration |
Overview:
Until recently, modeling and quantifying the effectiveness of military operations in a dynamic theatre environment was nearly impossible. Multi-Agent Simulation (MAS) offers an environment in which small-to large-scale joint war fighting scenarios can be constructed and explored. The inherent uncertainty of war game outcomes make evolutionary algorithms ideally suited to the design and optimization of cooperative military systems, system-of-systems, and individual agent behavior. They also provide exploratory capabilities analysis and open the door to predicting how an adversary might co-evolve in response to newly developed war fighting technologies.
Evolutionary (genetic) algorithms work directly with discrete design variables and are well-suited for discontinuously and noisy search spaces. Darwin is a genetic algorithm integrated directly with PHX ModelCenter.
This Webcast will include:
- Functional overview of PHX ModelCenter®
- Functional overview of Darwin Plug-In
- Explanation of Multi-Agent Simulation (MAS) with regards to joint war fighting scenarios
- Functional overview of tools used to perform MAS
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