- author = "Noor Shaker",
- title = "Towards Player-Driven Procedural Content Generation",
- school = "Center for Computer Games Research, IT University of Copenhagen",
- year = "2012",
- address = "Denmark",
- month = dec,
- keywords = "genetic algorithms, genetic programming, grammatical evolution",
- isbn13 = "978-87-7949-285-1",
- ISSN = "1602-3536",
-
URL = "
https://pure.itu.dk/portal/en/publications/towards-playerdriven-procedural-content-generation(4b414e6a-4fb7-4aa9-b2f4-5fc137ee08f7).html",
-
URL = "
https://pure.itu.dk/portal/files/39501027/thesis_Noor_Shaker.pdf",
- size = "288 pages",
-
abstract = "Generating immersive game content is one of the
ultimate goals for a game designer. This goal can be
achieved taken into account that players perceptions of
the same game differ according to a number of factors
including: players personality, playing styles,
expertise and cultural background. One promising avenue
towards optimising the game play experience for
individual game players (and thereby attempt to close
the affective loop in games) is to automatically tailor
the game content in real-time. To realise player-driven
procedural content generation one needs to specify the
aspects of the game that have a key influence on the
gameplay experience, identify the relationship between
these aspects and player experience and define a
mechanism for tailoring the game content to each
individual needs.
we attempt to address the following research questions towards the aim of generating personalised content for the player: how can we measure player experience, how can we represent game content, playing style and the in-game interaction, what features should be used to capture player experience and how can they be extracted, how can we model the unknown function between game content, player behavior and affect, how can we generate game content that is tailored to particular player needs and style, how often game content should be adapted, and how the adaptation mechanism can be tested?
We focus on 2D platform game genre as a test-bed for our player-driven procedural content generation framework. Crowd-sourcing experiments are designed to collect gameplay data, subjective and objective indicators of experience from human players: three datasets differing in the number of participants and the types of features collected and analyzed. Computational models of player experience are built on game content, gameplay, and visual reaction features capturing various aspects of the ingame interaction. Different forms of representation are considered for capturing frequencies, temporal and spatial content and behavioral events.
As soon as models of player experience are built, a real-time adaptation framework is designed which is guided by the models. The models are used as heuristics in the search of personalized content. Two adaptation mechanisms were tested in this thesis: the first is based on exhaustive search while genetic search is employed in the second. The mechanisms were tested with artificial agents and humans players.
The key findings of the thesis demonstrate the ability of the player-driven procedural content generation framework to recognise playing behavior differences and to generate player-centered content that optimizes particular aspects of player experience.",
-
notes = "Series ITU-DS, Number 90.
Supervisors: Georgios Yannakakis and Julian Togelius",
