Multiagent Evaluation under Incomplete Information
The work proposes novel graph-based solutions to accurately rank agents in noisy, many-player games where full payoff information is missing. It derives rigorous sample complexity guarantees for ranking accuracy and introduces adaptive sampling algorithms that intelligently select the next game outcome to observe, improving evaluation efficiency.