• Corpus ID: 239049672

On games and simulators as a platform for development of artificial intelligence for command and control

@article{Goecks2021OnGA,
  title={On games and simulators as a platform for development of artificial intelligence for command and control},
  author={Vinicius G. Goecks and Nicholas R. Waytowich and Derrik E. Asher and Song Jun Park and Mark R. Mittrick and John Richardson and Manuel M. Vindiola and Anne Logie and Mark S. Dennison and Theron Trout and Priya Narayanan and Alexander S. Kott},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.11305}
}
Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: multiple participants manage resources and make decisions that command assets to secure specific areas of a map or neutralize opposing forces. These characteristics have attracted the artificial intelligence (AI) community by supporting development of algorithms with complex benchmarks and the capability to rapidly… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 69 REFERENCES
Grandmaster level in StarCraft II using multi-agent reinforcement learning
TLDR
The agent, AlphaStar, is evaluated, which uses a multi-agent reinforcement learning algorithm and has reached Grandmaster level, ranking among the top 0.2% of human players for the real-time strategy game StarCraft II.
Algorithmically identifying strategies in multi-agent game-theoretic environments
TLDR
This paper focuses on an algorithmic approach to extract group strategies from multi-agent teaming behaviors in a game-theoretic environment: predator-prey pursuit and may lead to the design of agents that can recognize and fall in line with strategies implicitly adopted by human teammates.
Reinforcement learning framework for collaborative agents interacting with soldiers in dynamic military contexts
TLDR
This work proposes a three-armed approach to the development of agent teammates that leverages advances in deep reinforcement learning, multi-agent deep learning, and human-guided reinforcement to constrain agent behavior and speed up the discovery of optimal strategies.
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
TLDR
A new simulator built on Unreal Engine that offers physically and visually realistic simulations for autonomous vehicles in real world and that is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols.
Developing Combat Behavior through Reinforcement Learning in Wargames and Simulations
TLDR
This research explored the ability of RL to train AI agents to achieve optimal offensive behavior in small tactical engagements and showed the combat model and RL algorithm applied had the largest impact on training performance.
Mastering the game of Go with deep neural networks and tree search
TLDR
Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
StarCraft II: A New Challenge for Reinforcement Learning
TLDR
This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game that offers a new and challenging environment for exploring deep reinforcement learning algorithms and architectures and gives initial baseline results for neural networks trained from this data to predict game outcomes and player actions.
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game
TLDR
This is the first public work to investigate AI agents that can defeat the built-in AI in the StarCraft II full game, and the AI agent TStarBot1 is based on deep reinforcement learning over a flat action structure and theAI agent T starBot2 isbased on hard-coded rules over a hierarchical action structure.
Mastering the game of Go without human knowledge
TLDR
An algorithm based solely on reinforcement learning is introduced, without human data, guidance or domain knowledge beyond game rules, that achieves superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.
Reading the Mind of the Enemy: Predictive Analysis and Command Effectiveness
Abstract : The Defense Advanced Research Projects Agency (DARPA) Real-time Adversarial Intelligence and Decision-making (RAID) program is investigating the feasibility of reading the mind of the
...
1
2
3
4
5
...