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

Author

Listed:
  • Vinicius G Goecks
  • Nicholas Waytowich
  • Derrik E Asher
  • Song Jun Park
  • Mark Mittrick
  • John Richardson
  • Manuel Vindiola
  • Anne Logie
  • Mark Dennison
  • Theron Trout
  • Priya Narayanan
  • Alexander Kott

Abstract

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 iterate over new ideas. The success of AI algorithms in real-time strategy games such as StarCraft II has also attracted the attention of the military research community aiming to explore similar techniques in military counterpart scenarios. Aiming to bridge the connection between games and military applications, this work discusses past and current efforts on how games and simulators, together with the AI algorithms, have been adapted to simulate certain aspects of military missions and how they might impact the future battlefield. This paper also investigates how advances in virtual reality and visual augmentation systems open new possibilities in human interfaces with gaming platforms and their military parallels.

Suggested Citation

  • Vinicius G Goecks & Nicholas Waytowich & Derrik E Asher & Song Jun Park & Mark Mittrick & John Richardson & Manuel Vindiola & Anne Logie & Mark Dennison & Theron Trout & Priya Narayanan & Alexander Ko, 2023. "On games and simulators as a platform for development of artificial intelligence for command and control," The Journal of Defense Modeling and Simulation, , vol. 20(4), pages 495-508, October.
    • Handle: RePEc:sae:joudef:v:20:y:2023:i:4:p:495-508
    DOI: 10.1177/15485129221083278
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    References listed on IDEAS

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    1. Julian Schrittwieser & Ioannis Antonoglou & Thomas Hubert & Karen Simonyan & Laurent Sifre & Simon Schmitt & Arthur Guez & Edward Lockhart & Demis Hassabis & Thore Graepel & Timothy Lillicrap & David , 2020. "Mastering Atari, Go, chess and shogi by planning with a learned model," Nature, Nature, vol. 588(7839), pages 604-609, December.
    2. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    3. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    4. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    Cited by:

    1. Ahaan Dabholkar & James Z Hare & Mark Mittrick & John Richardson & Nicholas Waytowich & Priya Narayanan & Saurabh Bagchi, 2026. "Adversarial attacks on reinforcement learning agents for command and control," The Journal of Defense Modeling and Simulation, , vol. 23(1), pages 177-190, January.

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