Created by W.Langdon from gp-bibliography.bib Revision:1.6811
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.",
Supervisors: Georgios Yannakakis and Julian Togelius",
Genetic Programming entries for Noor Shaker