NOTE: The following materials are presented for timely
dissemination of academic and technical work.
Copyright and all other rights
therein are reserved by authors and/or other copyright holders. Persoanl
use of the following materials is
permitted and, however, people using
the materials or information are expected to adhere to the terms and
constraints invoked by the related
copyright.
Learning-Based Procedural Content Generation
ABSTRACT
Procedural content generation (PCG) has recently
become one of the hottest topics in computational intelligence and
AI game researches. While some substantial progress has been
made in this area, there are still several challenges ranging from
content evaluation to personalized content generation. In this
paper, we present a novel PCG framework based on machine
learning, named learning-based procedure content generation
(LBPCG), to tackle a number of challenging problems. By
exploring and exploiting information gained in game development
and public player test, our framework can generate robust
content adaptable to end-user or target players on-line with
minimal interruption to their gameplay experience. As the datadriven
methodology is emphasized in our framework, we develop
learning-based enabling techniques to implement the various
models required in our framework. For a proof of concept, we
have developed a prototype based on the classic open source firstperson
shooter game, Quake. Simulation results suggest that our
framework is promising in generating quality content.
Click
tciaig2015.pdf
for full text. For playing our personalised Quake games, go to LBPCG-Quake.