An Online System for Player-vs-Level Matchmaking in Human Computation Games

We present a fully online system for skill and ratings-based dynamic difficulty adjustment (DDA) in human computation games (RCGs). Prior work used an initial offline phase for collecting level ratings to avoid cold-start when using the system online. Further, such systems set a fixed target score win/lose threshold for each level. In this work, we address these issues by 1) using an ∊-greedy variant of the ratings and skill-based DDA algorithm and 2) applying rating arrays, proposed in past work but not yet used in an online setting. We demonstrate our system in two online matchmaking experiments using two HCGs. Results suggest that the ∊-greedy modification helps address the cold-start and that using rating arrays leads to players completing more levels.