Beyond Skill Rating: Advanced Matchmaking in Ghost Recon Online

被引:40
作者
Delalleau, Olivier [1 ]
Contal, Emile [2 ]
Thibodeau-Laufer, Eric [1 ]
Ferrari, Raul Chandias [1 ]
Bengio, Yoshua [1 ]
Zhang, Frank [3 ]
机构
[1] Univ Montreal, Dept Comp Sci & Operat Res, Montreal, PQ H3C 3J7, Canada
[2] Ecole Normale Super, Dept Comp Sci, F-94235 Cachan, France
[3] Ubisoft Montreal, Montreal, PQ H2T 1S6, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
First-person shooters (FPSs); game balance; matchmaking; neural networks; player satisfaction; CLASSIFICATION; PLAYERS; SYSTEM; MODEL;
D O I
10.1109/TCIAIG.2012.2188833
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Player satisfaction is particularly difficult to ensure in online games, due to interactions with other players. In adversarial multiplayer games, matchmaking typically consists in trying to match together players of similar skill level. However, this is usually based on a single-skill value, and assumes the only factor of "fun" is the game balance. We present a more advanced matchmaking strategy developed for Ghost Recon Online, an upcoming team-focused first-person shooter (FPS) from Ubisoft (Montreal, QC, Canada). We first show how incorporating more information about players than their raw skill can lead to more balanced matches. We also argue that balance is not the only factor that matters, and present a strategy to explicitly maximize the players' fun, taking advantage of a rich player profile that includes information about player behavior and personal preferences. Ultimately, our goal is to ask players to provide direct feedback on match quality through an in-game survey. However, because such data were not available for this study, we rely here on heuristics tailored to this specific game. Experiments on data collected during Ghost Recon Online's beta tests show that neural networks can effectively be used to predict both balance and player enjoyment.
引用
收藏
页码:167 / 177
页数:11
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