Abstract
What are the aesthetics of platform games and
what makes a platform level engaging, challenging and/or frustrating? We attempt to answer such questions through mining a
large-set of crowd-sourced gameplay data of a clone of the classic
platform game Super Mario Bros. The data consists of 40 short
game levels that differ along six key level design parameters.
Collectively, these levels are played 1560 times over the Internet
and the perceived experience is annotated by experiment
participants via self-reported ranking (pairwise preferences).
Given the wealth of this crowd-sourced data, as all details
about players’ in-game behaviour are logged, the problem
becomes one of extracting meaningful numerical features at the
appropriate level of abstraction for the construction of generic
computational models of player experience and, thereby, game
aesthetics. We explore dissimilar types of features, including
direct measurements of event and item frequencies, and features
constructed through frequent sequence mining and go through
an in-depth analysis of the interrelationship between level
content, player’s behavioural patterns and reported experience.
Furthermore, the fusion of the extracted features allows us to
predict reported player experience with a high accuracy even
from short game segments. In addition to advancing our insight
on the factors that contribute to platform game aesthetics, the
results are useful for the personalisation of game experience via
automatic game adaptation.
what makes a platform level engaging, challenging and/or frustrating? We attempt to answer such questions through mining a
large-set of crowd-sourced gameplay data of a clone of the classic
platform game Super Mario Bros. The data consists of 40 short
game levels that differ along six key level design parameters.
Collectively, these levels are played 1560 times over the Internet
and the perceived experience is annotated by experiment
participants via self-reported ranking (pairwise preferences).
Given the wealth of this crowd-sourced data, as all details
about players’ in-game behaviour are logged, the problem
becomes one of extracting meaningful numerical features at the
appropriate level of abstraction for the construction of generic
computational models of player experience and, thereby, game
aesthetics. We explore dissimilar types of features, including
direct measurements of event and item frequencies, and features
constructed through frequent sequence mining and go through
an in-depth analysis of the interrelationship between level
content, player’s behavioural patterns and reported experience.
Furthermore, the fusion of the extracted features allows us to
predict reported player experience with a high accuracy even
from short game segments. In addition to advancing our insight
on the factors that contribute to platform game aesthetics, the
results are useful for the personalisation of game experience via
automatic game adaptation.
| Original language | English |
|---|---|
| Journal | I E E E Transactions on Computational Intelligence and A I in Games |
| Volume | 5 |
| Issue number | 3 |
| Pages (from-to) | 276 - 290 |
| ISSN | 1943-068X |
| Publication status | Published - Sept 2014 |