Predicting Game Difficulty and Churn Without Players

Shaghayegh Roohi, Asko Relas, Jari Takatalo, Henri Heiskanen, Perttu Hämäläinen

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

6 Sitaatiot (Scopus)
128 Lataukset (Pure)

Abstrakti

We propose a novel simulation model that is able to predict the per-level churn and pass rates of Angry Birds Dream Blast, a popular mobile free-to-play game. Our primary contribution is to combine AI gameplay using Deep Reinforcement Learning (DRL) with a simulation of how the player population evolves over the levels. The AI players predict level difficulty, which is used to drive a player population model with simulated skill, persistence, and boredom. This allows us to model, e.g., how less persistent and skilled players are more sensitive to high difficulty, and how such players churn early, which makes the player population and the relation between difficulty and churn evolve level by level. Our work demonstrates that player behavior predictions produced by DRL gameplay can be significantly improved by even a very simple population-level simulation of individual player differences, without requiring costly retraining of agents or collecting new DRL gameplay data for each simulated player.
AlkuperäiskieliEnglanti
OtsikkoCHI PLAY 2020 - Proceedings of the Annual Symposium on Computer-Human Interaction in Play
KustantajaACM
Sivut585-593
Sivumäärä9
ISBN (elektroninen)9781450380744
ISBN (painettu)9781450380744
DOI - pysyväislinkit
TilaJulkaistu - 2 marraskuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaACM SIGCHI Annual Symposium on Computer-Human Interaction in Play - Virtual, Online, Kanada
Kesto: 1 marraskuuta 20204 marraskuuta 2020
Konferenssinumero: 7

Conference

ConferenceACM SIGCHI Annual Symposium on Computer-Human Interaction in Play
LyhennettäCHI PLAY
Maa/AlueKanada
KaupunkiVirtual, Online
Ajanjakso01/11/202004/11/2020

Sormenjälki

Sukella tutkimusaiheisiin 'Predicting Game Difficulty and Churn Without Players'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä