To enable large-scale multi-agent coordination under temporal and spatial constraints, we formulate it as a multi-level optimization problem and develop a multi-abstraction search approach for cooptimizing agent placement with task assignment and scheduling. This approach begins with a highly abstract agent placement problem and the rapid computation of an initial solution, which is then improved upon using a hill climbing algorithm for a less abstract problem; finally, the solution is fine-tuned within the original problem space. Empirical results demonstrate that this multi-abstraction approach significantly outperforms a conventional hill climbing algorithm and an approximate mixed integer linear programming approach
