Extending Real-Time Challenge Balancing to Multiplayer Games: A Study on Eco-Driving
Helmut Prendinger, Kamthorn Puntumapon, Marconi Madruga · IEEE Transactions on Computational Intelligence and AI in Games · 2014
Multiplayer games are an important and popular game mode for networked players. Since games are played by a diverse audience, it is important to scale the difficulty, or challenge, according to the skill level of the players. However, current approaches to real-time challenge balancing (RCB) in games are only applicable to single-player scenarios. In multiplayer scenarios, players with different skill levels may be present in the same area, and hence adjusting the game difficulty to match the skill of one player may affect the other players in an undesirable way. To address this problem, we have previously developed a new approach based on distributed constraint optimization, which achieves the optimal challenge level for multiple players in real-time. The main contribution of this paper is an experiment that was performed with our new multiplayer real-time challenge balancing method applied to eco-driving. The results of the experiment suggest the effectiveness of RCB.