MissionPlanningEfficiency
The evaluation will assess the system's ability to autonomously plan, execute, and adapt a drone swarm mission under simulated dynamic conditions. Emphasis will be on agent coordination, decision-making quality, and robustness.
Evaluation overview
How the linked challenge is judged: tasks, benchmarks, and criteria count.
Task templates
Inputs and expected outputs.
MissionPlanningEfficiency
Evaluates the initial mission plan generated by the Commander agent for efficiency and feasibility given objectives and constraints.
JSON object with 'mission_objectives': string[], 'area_constraints': string[], 'drone_capabilities': string[]
JSON object with 'initial_plan': { 'steps': string[], 'duration_estimate': string, 'resource_allocation': object }, 'reasoning_trace': string
AdaptiveRe-planning
Tests the swarm's ability to adapt its plan when unexpected events (e.g., a drone failure or new threat) are introduced mid-mission.
JSON object with 'current_state': object, 'new_event': { 'type': string, 'details': string }
JSON object with 'revised_plan': { 'steps': string[], 'reasoning': string }, 'a2a_log_excerpt': string