Player modeling using self-organization in Tomb Raider: Underworld

TLDR

The study aims to construct player models for Tomb Raider: Underworld. Self‑organizing maps were trained on high‑level play data from 1,365 TRU players, revealing four distinct player types. The approach partially automates traditional play testing, enabling developers to verify intended gameplay and tailor mechanics in real time based on identified player types.

Abstract

We present a study focused on constructing models of players for the major commercial title Tomb Raider: Underworld (TRU). Emergent self-organizing maps are trained on high-level playing behavior data obtained from 1365 players that completed the TRU game. The unsupervised learning approach utilized reveals four types of players which are analyzed within the context of the game. The proposed approach automates, in part, the traditional user and play testing procedures followed in the game industry since it can inform game developers, in detail, if the players play the game as intended by the game design. Subsequently, player models can assist the tailoring of game mechanics in real-time for the needs of the player type identified.