Predicting skill from gameplay input to a first-person shooter

David Buckley; Ke Chen; Joshua Knowles

doi:10.1109/CIG.2013.6633655

Predicting skill from gameplay input to a first-person shooter

David Buckley, Ke Chen, Joshua Knowles

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

537 Downloads (Pure)

Abstract

One way to make video games more attractive to a wider audience is to make them adaptive to players. The preferences and skills of players can be determined in a variety of ways, but should be done as unobtrusively as possible to keep the player immersed. This paper explores how gameplay input recorded in a first-person shooter can predict a player's ability. As these features were able to model a player's skill with 76% accuracy, without the use of game-specific features, we believe their use would be transferable across similar games within the genre. © 2013 IEEE.

Original language	English
Title of host publication	IEEE Conference on Computatonal Intelligence and Games, CIG\|IEEE Conf. Comput. Intell. Games, CIG
Place of Publication	U.S.A.
Publisher	IEEE
ISBN (Print)	9781467353113
DOIs	https://doi.org/10.1109/CIG.2013.6633655 https://doi.org/10.1109/CIG.2013.6633655
Publication status	Published - 2013
Event	2013 IEEE Conference on Computational Intelligence in Games, CIG 2013 - Niagara Falls, ON Duration: 1 Jul 2013 → …

Conference

Conference	2013 IEEE Conference on Computational Intelligence in Games, CIG 2013
City	Niagara Falls, ON
Period	1/07/13 → …

Access to Document

FULL-TEXT

Cite this

@inproceedings{b0b87f0c2087414a84255237d83d82b7,

title = "Predicting skill from gameplay input to a first-person shooter",

abstract = "One way to make video games more attractive to a wider audience is to make them adaptive to players. The preferences and skills of players can be determined in a variety of ways, but should be done as unobtrusively as possible to keep the player immersed. This paper explores how gameplay input recorded in a first-person shooter can predict a player's ability. As these features were able to model a player's skill with 76% accuracy, without the use of game-specific features, we believe their use would be transferable across similar games within the genre. {\textcopyright} 2013 IEEE.",

author = "David Buckley and Ke Chen and Joshua Knowles",

year = "2013",

doi = "10.1109/CIG.2013.6633655",

language = "English",

isbn = "9781467353113",

booktitle = "IEEE Conference on Computatonal Intelligence and Games, CIG|IEEE Conf. Comput. Intell. Games, CIG",

publisher = "IEEE",

address = "United States",

note = "2013 IEEE Conference on Computational Intelligence in Games, CIG 2013 ; Conference date: 01-07-2013",

}

Buckley, D, Chen, K & Knowles, J 2013, Predicting skill from gameplay input to a first-person shooter. in IEEE Conference on Computatonal Intelligence and Games, CIG|IEEE Conf. Comput. Intell. Games, CIG. IEEE, U.S.A., 2013 IEEE Conference on Computational Intelligence in Games, CIG 2013, Niagara Falls, ON, 1/07/13. https://doi.org/10.1109/CIG.2013.6633655, https://doi.org/10.1109/CIG.2013.6633655

TY - GEN

T1 - Predicting skill from gameplay input to a first-person shooter

AU - Buckley, David

AU - Chen, Ke

AU - Knowles, Joshua

PY - 2013

Y1 - 2013

N2 - One way to make video games more attractive to a wider audience is to make them adaptive to players. The preferences and skills of players can be determined in a variety of ways, but should be done as unobtrusively as possible to keep the player immersed. This paper explores how gameplay input recorded in a first-person shooter can predict a player's ability. As these features were able to model a player's skill with 76% accuracy, without the use of game-specific features, we believe their use would be transferable across similar games within the genre. © 2013 IEEE.

AB - One way to make video games more attractive to a wider audience is to make them adaptive to players. The preferences and skills of players can be determined in a variety of ways, but should be done as unobtrusively as possible to keep the player immersed. This paper explores how gameplay input recorded in a first-person shooter can predict a player's ability. As these features were able to model a player's skill with 76% accuracy, without the use of game-specific features, we believe their use would be transferable across similar games within the genre. © 2013 IEEE.

UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-84892400987&partnerID=MN8TOARS

U2 - 10.1109/CIG.2013.6633655

DO - 10.1109/CIG.2013.6633655

M3 - Conference contribution

SN - 9781467353113

BT - IEEE Conference on Computatonal Intelligence and Games, CIG|IEEE Conf. Comput. Intell. Games, CIG

PB - IEEE

CY - U.S.A.

T2 - 2013 IEEE Conference on Computational Intelligence in Games, CIG 2013

Y2 - 1 July 2013

ER -

Predicting skill from gameplay input to a first-person shooter

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Cite this