Planning the delivery of cargo as part of an airlift operation is a notoriously complex problem. Transportation routes can suddenly become inaccessible due to poor weather or other unexpected occurrences. Factors such as airplane speed, carrying capacity, and airport maximum-on-ground must also be considered to ensure on-time and efficient delivery. Unforeseen cargo needs may require quick re-planning to meet tight deadlines. To address the problem, we held the Airlift Challenge, an online multi-agent planning competition which concluded in early 2023. Competitors in the Airlift Challenge created innovative algorithms to execute a simulated airlift operation. The algorithms were scored against a set of increasingly complex evaluation scenarios while contending with unexpected events and disruptions. In this paper, we describe the competition and simulation environment, summarize results, and include write-ups of the top approaches provided by the winning teams.
In this paper we explore the application of recent advances in AI/algorithmic game theory for autonomous tactical maneuvering in aerial combat mission scenarios. Tactical maneuvering requires autonomous aircrafts to learn defensive/offensive tactics while reasoning about an intelligent adversary, making it a challenging decision making problem. We consider a one-vs-one aerial dogfighting scenario and formulate it as a two-person zero-sum perfect information game. To solve this game online we apply simultaneous move Monte Carlo Tree Search (MCTS) since both aircrafts simultaneously take maneuvering decisions to gain tactical advantage. Compared to other techniques, MCTS enables efficient search over long horizons and uses self-play to select best maneuver in the current state while accounting for the opponent aircraft tactics. We explore different algorithmic choices in MCTS and demonstrate the framework numerically in a simulated 2D tactical maneuvering application.
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