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Task Allocation with Load Management in Multi-Agent Teams
In this work, we present a decision-making framework for multi-agent teams to learn task allocation with the consideration of load management through decentralized reinforcement learning, where idling is encouraged and unnecessary resource usage is avoided.
Analysis of the Synergy between Modularity and Autonomy in an Artificial Intelligence Based Fleet Competition
A novel approach is provided for evaluating the benefits and burdens from vehicle modularity in fleets/units through the analysis of a game theoretical model of the competition between autonomous vehicle fleets in an attacker-defender game.
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.
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