103,669 research outputs found
Experience-driven procedural content generation (extended abstract)
Procedural content generation is an increasingly
important area of technology within modern human-computer
interaction with direct applications in digital games, the semantic
web, and interface, media and software design. The personalization
of experience via the modeling of the user, coupled with the
appropriate adjustment of the content according to user needs
and preferences are important steps towards effective and meaningful
content generation. This paper introduces a framework for
procedural content generation driven by computational models of
user experience we name Experience-Driven Procedural Content
Generation. While the framework is generic and applicable to
various subareas of human computer interaction, we employ
games as an indicative example of content-intensive software that
enables rich forms of interaction.The research was supported, in part, by the FP7 ICT projects
C2Learn (318480) and iLearnRW (318803).peer-reviewe
The experience-driven perspective
Ultimately, content is generated for the player. But so far, our algorithms
have not taken specific players into account. Creating computational models of a
player’s behaviour, preferences, or skills is called player modelling. With a model
of the player, we can create algorithms that create content specifically tailored to
that player. The experience-driven perspective on procedural content generation provides
a framework for content generation based on player modelling; one of the most
important ways of doing this is to use a player model in the evaluation function for
search-based PCG. This chapter discusses different ways of collecting and encoding
data about the player, primarily player experience, and ways of modelling this data.
It also gives examples of different ways in which such models can be used.peer-reviewe
Online Game Level Generation from Music
Game consists of multiple types of content, while the harmony of different
content types play an essential role in game design. However, most works on
procedural content generation consider only one type of content at a time. In
this paper, we propose and formulate online level generation from music, in a
way of matching a level feature to a music feature in real-time, while adapting
to players' play speed. A generic framework named online player-adaptive
procedural content generation via reinforcement learning, OPARL for short, is
built upon the experience-driven reinforcement learning and controllable
reinforcement learning, to enable online level generation from music.
Furthermore, a novel control policy based on local search and k-nearest
neighbours is proposed and integrated into OPARL to control the level generator
considering the play data collected online. Results of simulation-based
experiments show that our implementation of OPARL is competent to generate
playable levels with difficulty degree matched to the ``energy'' dynamic of
music for different artificial players in an online fashion
State Space Closure: Revisiting Endless Online Level Generation via Reinforcement Learning
In this paper, we revisit endless online level generation with the recently
proposed experience-driven procedural content generation via reinforcement
learning (EDRL) framework. Inspired by an observation that EDRL tends to
generate recurrent patterns, we formulate a notion of state space closure which
makes any stochastic state appeared possibly in an infinite-horizon online
generation process can be found within a finite-horizon. Through theoretical
analysis, we find that even though state space closure arises a concern about
diversity, it generalises EDRL trained with a finite-horizon to the
infinite-horizon scenario without deterioration of content quality. Moreover,
we verify the quality and the diversity of contents generated by EDRL via
empirical studies, on the widely used Super Mario Bros. benchmark. Experimental
results reveal that the diversity of levels generated by EDRL is limited due to
the state space closure, whereas their quality does not deteriorate in a
horizon which is longer than the one specified in the training. Concluding our
outcomes and analysis, future work on endless online level generation via
reinforcement learning should address the issue of diversity while assuring the
occurrence of state space closure and quality.Comment: Accepted by the IEEE Transactions on Game
Learning the Designer's Preferences to Drive Evolution
This paper presents the Designer Preference Model, a data-driven solution
that pursues to learn from user generated data in a Quality-Diversity
Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the
user's design style to better assess the tool's procedurally generated content
with respect to that user's preferences. Through this approach, we aim for
increasing the user's agency over the generated content in a way that neither
stalls the user-tool reciprocal stimuli loop nor fatigues the user with
periodical suggestion handpicking. We describe the details of this novel
solution, as well as its implementation in the MI-CC tool the Evolutionary
Dungeon Designer. We present and discuss our findings out of the initial tests
carried out, spotting the open challenges for this combined line of research
that integrates MI-CC with Procedural Content Generation through Machine
Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European
Conference on the Applications of Evolutionary and bio-inspired Computation,
EvoApplications 202
An Integrated Framework for AI Assisted Level Design in 2D Platformers
The design of video game levels is a complex and critical task. Levels need
to elicit fun and challenge while avoiding frustration at all costs. In this
paper, we present a framework to assist designers in the creation of levels for
2D platformers. Our framework provides designers with a toolbox (i) to create
2D platformer levels, (ii) to estimate the difficulty and probability of
success of single jump actions (the main mechanics of platformer games), and
(iii) a set of metrics to evaluate the difficulty and probability of completion
of entire levels. At the end, we present the results of a set of experiments we
carried out with human players to validate the metrics included in our
framework.Comment: Submitted to the IEEE Game Entertainment and Media Conference 201
- …