Analysis of Fleet Modularity in an Artificial Intelligence-Based
  Attacker-Defender Game
  Because combat environments change over time and technology upgrades are
widespread for ground vehicles, a large number of vehicles and equipment become
quickly obsolete. A possible solution for the U.S. Army is to develop fleets of
modular military vehicles, which are built by interchangeable substantial
components also known as modules. One of the typical characteristics of module
is their ease of assembly and disassembly through simple means such as
plug-in/pull-out actions, which allows for real-time fleet reconfiguration to
meet dynamic demands. Moreover, military demands are time-varying and highly
stochastic because commanders keep reacting to enemy's actions. To capture
these characteristics, we formulated an intelligent agent-based model to
imitate decision making process during fleet operation, which combines
real-time optimization with artificial intelligence. The agents are capable of
inferring enemy's future move based on historical data and optimize
dispatch/operation decisions accordingly. We implement our model to simulate an
attacker-defender game between two adversarial and intelligent players,
representing the commanders from modularized fleet and conventional fleet
respectively. Given the same level of combat resources and intelligence, we
highlight the tactical advantages of fleet modularity in terms of win rate,
unpredictability and suffered damage.
