Exploring Behavioral Clustering for Player Matchmaking in Multiplayer Games

Recent research highlights that beyond skill ratings, players behavioral tendencies also play a crucial role in shaping gameplay experience. This study proposes a behavior-based clustering approach for matchmaking. It investigates how grouping players by behavioral similarity shapes interaction dynamics and system-level trade-offs. Using AI agents as proxies for players, four archetypal strategiesaggressive, defensive, cooperative, and non-cooperativewere modeled. Agents were clustered by behavioral features and matched either randomly (baseline) or by cluster similarity. The experiments, conducted over multiple runs in a simplified environment, compared outcomes at both the player and room levels using descriptive statistics and composite indices of diversity, balance, and richness. Results indicate that clustering changes interaction patterns rather than uniformly improving or degrading outcomes; notably, both defensive and aggressive agents showed reduced attack frequencies under similarity-based matching, suggesting environment-induced strategy adaptation. While the simulation is constrained and archetypes are simplified, the results suggest that behavior-aware matchmaking policies can optimize diversity, balance, and richness in practical deployment.