Predicting unexpected influxes of players in EVE online

Abstract

EVE Online is a massively multiplayer online role-playing game (MMORPG) taking place in a large galaxy consisting of about 7 500 star systems. In comparison to many other online role-playing games, the users interact in the same instance of a persistent player-driven universe. Given the number of simultaneous pilots online at the same time - a number which at times reaches up to more than 50 000 concurrent accounts logged on to the same server - the EVE Online universe can present atypically difficult load-balancing challenges when the users decide to operate in a coordinated fashion, for example, to launch an attack on a particular system. We will present an scalable, automated statistical method for predicting such unexpected user gatherings by considering the evolving shortest-path distances from each user to each system. Here we present a case study analyzing nearly 300 million user movements in the EVE Online universe from over 700 thousand user accounts over a period of three months. We demonstrate an ability to predict sudden spikes in user presence (corresponding to actual events) before they happen, suggesting our techniques could be useful for automated load-balancing in such massive online games

Citation

Garnett, R., Gartner, T., Ellersiek, T., Guðmondsson, E., & Óskarsson, P. (2014). Predicting unexpected influxes of players in EVE online. In IEEE Conference on Computational Intelligence and Games 2014: Proceedings. https://doi.org/10.1109/CIG.2014.6932878

• Conference Name: IEEE Conference on Computational Intelligence and Games
• Start Date: Aug 26, 2014
• End Date: Aug 29, 2014
• Acceptance Date: Aug 1, 2014
• Online Publication Date: Aug 26, 2014
• Publication Date: Aug 26, 2014
• Deposit Date: Feb 16, 2017
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Book Title: IEEE Conference on Computational Intelligence and Games 2014: Proceedings
• DOI: https://doi.org/10.1109/CIG.2014.6932878
• Public URL: https://nottingham-repository.worktribe.com/output/1115517
• Publisher URL: https://ieeexplore.ieee.org/document/6932878/