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Advanced Matchmaking for Online First Person Shooter Games using Machine Learning
KTH, School of Information and Communication Technology (ICT).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Matchmaking is an essential part of many modern online multiplayer games and is used by game developers to give the players the best possible online gaming experience. However, since video games have become more complex, traditional matchmaking systems like TrueSkill have reached their limits in predicting match outcomes. An extensive literature survey on engagement and balance in video games as well as an analysis of Battlefield 4 player data showed that balance can have a significant impact on player engagement. This lays the basis for the new matchmaking approach that is presented in this thesis. It is developed for the online First Person Shooter game Battlefield 4, with the goal of increasing player engagement by balancing online multiplayer matches.

The developed matchmaking system is based on regression models, which use player performance metrics to predict the balance of online multiplayer matches. The experimental evaluations of the developed models show that the quality of the prediction results are influenced by the complexity of the different game modes available in Battlefield 4. Furthermore the historical Battlefield 4 game report data, which is used for building the predictive models, shows that this complexity as well as imbalances in the game design add significant noise to balance predictions. Both evaluated regression models – Linear Regression and Multivariate Adaptive Regression Splines – showed similar prediction errors within statistically expected deviation. Additionally it is shown that both methods have significantly smaller errors than the TrueSkill system, when predicting the outcome of games in Team Death Match or Conquest mode. The features that resulted in the lowest errors are commonly used in online First Person Shooter games. Hence the findings of this thesis can not only improve the matchmaking of Battlefield 4, but also benefit other video games of the same genre.

Abstract [sv]

Matchmaking är en viktig del av många onlinespel och används av spelutvecklare för att ge spelare en bättre spelupplevelse. På senare tid har spelen blivit mer komplexa och traditionella matchmakingsystem som TrueSkill klarar inte längre förutse utfall av matcher. Andra studier som avhandlar spelaktivitet och balans i matcher visar att spelare har ökad aktivitet om deras matcher har varit jämna. Det går även att dra samma slutsatser från speldata i Battlefield 4. Syftet med denna uppsats är att utveckla och utvärdera en ny modell för matchmaking i spelet Battlefield 4 som medför bättre balans i matcher och därmed ökar spelaktiviteten.

Den utvecklade matchmaking-modellen baseras på regressionmodeller som använder historiska speldata för enskilda spelare för att förutse jämnheten i matcher. Utvärderingen av den utvecklade modellen visar att förmågan att förutse matcher beror på hur hur komplext spelläget i Battlefield 4 är. Det framgår också efter att ha tittat på historiska spelrapporter att förutom komplexiteten i spelläget så påverkar också obalans i speldesignen förmågan att förutse matcher. Båda utvärderade regressionsmodeller – linjär regression och Multivariate Adaptive Regression Splines – har liknande förmåga att förutse utfallet av matcher där ingen är signifikant bättre än den andra. Dessutom visas att båda modellerna förutser matchresultaten för spellägena Team Death Match och Conquest signifikant bättre än TrueSkill. De här spellägena är vanliga i denna typen av spel så resultaten i denna uppsats är inte isolerade till Battlefield 4 utan går att applicera på många spel i samma genre.

Place, publisher, year, edition, pages
2015. , 70 p.
Series
TRITA-ICT-EX, 2015:147
Keyword [en]
Matchmaking, Player engagement, Game balance, Online First Person Shooter games, Machine learning
Keyword [sv]
Matchmaking, Spelaktivitet, Spelbalans, Online First Person Shooter games, Maskininlärning
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-177568OAI: oai:DiVA.org:kth-177568DiVA: diva2:873273
Examiners
Available from: 2015-11-25 Created: 2015-11-23 Last updated: 2017-06-15Bibliographically approved

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