Work

Player modeling using self-organization in Tomb Raider: Underworld

Year: 2009

Type: conference-paper

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.

Source: IEEE Conference on Computational Intelligence and Games (CIG)

DOI: 10.1109/cig.2009.5286500

Authors: Anders Drachen, Alessandro Canossa, Georgios N. Yannakakis

Institutions: IT University of Copenhagen, Copenhagen School of Design and Technology

Language: English

Topic: Artificial Intelligence in Games

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2146772569

FWCI: 16.2

Cites: 22

Cited by: 306

Related to: 10