Simulation optimization: a survey of simulation optimization techniques and procedures
Proceedings of the 32nd conference on Winter simulation
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Recent advances in ranking and selection
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
A Knowledge-Gradient Policy for Sequential Information Collection
SIAM Journal on Control and Optimization
A brief introduction to optimization via simulation
Winter Simulation Conference
Simulation model calibration with correlated knowledge-gradients
Winter Simulation Conference
Winter Simulation Conference
The Knowledge Gradient Algorithm for a General Class of Online Learning Problems
Operations Research
Optimal learning of transition probabilities in the two-agent newsvendor problem
Proceedings of the Winter Simulation Conference
Calibrating simulation models using the knowledge gradient with continuous parameters
Proceedings of the Winter Simulation Conference
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We consider the problem of automated match-making in a competitive online gaming service. Large numbers of players log on to the service and indicate their availability. The system must then find an opponent for each player, with the objective of creating competitive, challenging games that do not heavily favour either side, for as many players as possible. Existing mathematical models for this problem assume that each player has a skill level that is unknown to the game master. As more games are played, the game master's belief about player skills evolves according to a Bayesian learning model, allowing the game master to adaptively improve the quality of future games as information is being collected. We propose a new decision-making policy in this setting, based on the knowledge gradient concept from the literature on optimal learning. We conduct simulations to demonstrate the potential of this policy.