2018
DOI: 10.29007/dlq7
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Adapting the Predator-Prey Game Theoretic Environment to Army Tactical Edge Scenarios with Computational Multiagent Systems

Abstract: The historical origins of the game theoretic predator-prey pursuit problem can be traced back to Benda, et al., 1985 [1]. Their work adapted the predator-prey ecology problem into a pursuit environment which focused on the dynamics of cooperative behavior between predator agents. Modifications to the predator-prey ecology problem [2] have been implemented to understand how variations to predator [3] and prey [3][4][5] attributes, including communication [6], can modify dynamic interactions between entities tha… Show more

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Cited by 6 publications
(4 citation statements)
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“…Asher et al 37 proposed the adoption of militarily relevant tactics to a simulated predator-prey pursuit task that allowed for teams of deep RL agents to engage in various tactics through capability modifications such as decoys, traps, and camouflage. Furthermore, this research group has introduced methods for measuring coordination [38][39][40] toward a framework for integrating AI agents into mixed soldier-agent teams.…”
Section: Background and Related Workmentioning
confidence: 99%
“…Asher et al 37 proposed the adoption of militarily relevant tactics to a simulated predator-prey pursuit task that allowed for teams of deep RL agents to engage in various tactics through capability modifications such as decoys, traps, and camouflage. Furthermore, this research group has introduced methods for measuring coordination [38][39][40] toward a framework for integrating AI agents into mixed soldier-agent teams.…”
Section: Background and Related Workmentioning
confidence: 99%
“…This research implements the adversarial character of the hacker and victim using cloud simulation as a tactical landscape. This method establishes an environment to quantify the attacker/victim relationship as a predator-prey interaction [39].…”
Section: Challenges and Contributionsmentioning
confidence: 99%
“…Asher et al 37 proposed the adoption of militarily relevant tactics to a simulated predator-prey pursuit task that allowed for teams of deep reinforcement learning agents to engage in various tactics through capability modifications such as decoys, traps, and camouflage. Further, this research group has introduced methods for measuring coordination [38][39][40] towards a framework for integrating AI agents into mixed Soldier-agent teams 41,42 .…”
Section: Background and Related Workmentioning
confidence: 99%