Work

Discovering Player Behavior From Gameplay Logs Using GBM, a Gameplay Process Discovery Algorithm

Year: 2026

Type: article

Abstract: In recent years, computer games have become a leading form of entertainment. To stand out in the market, developers seek tools to enhance gameplay experience. One way to improve the players′ overall perception of the game is to provide the appropriate design and balance of game mechanics. The effective approach to this problem, though challenging due to the distributed nature of modern games, is to use process mining to discover player behavioral patterns. This paper proposes the player behavioral miner algorithm, which analyzes distributed gameplay logs to identify such patterns. The resulting process models support automated improvement of game balance and immersion. Moreover, such algorithmically derived model can be seen as a digital twin that emulates player behavioral dynamics, enabling large‐scale meta‐analysis of gameplay patterns and performance strategies across diverse player cohorts. Experimental evaluation demonstrates that the proposed method outperforms existing solutions in terms of prediction accuracy.

Source: International Journal of Computer Games Technology

DOI: 10.1155/ijcg/3009547

Authors: Andrzej Stroinski, Adam Godzinski, Dariusz Brzezinski, Anna Kobusinska, Aleksander Stroinski

Institutions: Poznan Supercomputing and Networking Center, Poznań University of Technology

Language: English

Topic: Business Process Modeling and Analysis

Subfield: Management Information Systems

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,000

Funder: Narodowe Centrum Badań i Rozwoju