Detecting long-range cause-effect relationships in game provenance graphs with graph-based representation learning

• Published in: Entertainment computing 2019-12, Vol.32, p.100318, Article 100318
• Main Authors: Melo, Sidney Araujo, Paes, Aline, Clua, Esteban Walter Gonzalez, Kohwalter, Troy Costa, Murta, Leonardo Gresta Paulino
• Format: Article
• Language: English
• Subjects: Graph embeddings; Machine learning; Provenance graph; Representation learning
• ISSN: 1875-9521; 1875-953X
• DOI: 10.1016/j.entcom.2019.100318

•Presents an original attempt to combine machine learning and game provenance.•A framework that uses graph representation learning to improve game provenance data.•Presents the application of the framework in two game prototypes provenance datasets.•Evaluation of models generated for edge detection in graph datasets of two games. Game Analytics comprises a set of techniques to analyze both the game quality and player behavior. To succeed in Game Analytics, it is essential to identify what is happening in a game (an effect) and track its causes. Thus, game provenance graph tools have been proposed to capture cause-and-effect relationships occurring in a gameplay session to assist the game design process. However, since game provenance data capture is guided by a set of strict predefined rules established by the game developers, the detection of long-range cause-and-effect relationships may demand huge coding efforts. In this paper, we contribute with a framework named PingUMiL that leverages the recently proposed graph embeddings to represent game provenance graphs in a latent space. The embeddings learned from the data pose as the features of a machine learning task tailored towards detecting long-range cause-and-effect relationships. We evaluate the generalization capacity of PingUMiL when learning from similar games and compare its performance to classical machine learning methods. The experiments conducted on two racing games show that (1) PingUMiL outperforms classical machine learning methods and (2) representation learning can be used to detect long-range cause-and-effect relationships in only partially observed game data provenance graphs.