225 research outputs found
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
Towards Friendly Mixed Initiative Procedural Content Generation: Three Pillars of Industry
While the games industry is moving towards procedural content
generation (PCG) with tools available under popular platforms such as Unreal, Unity or Houdini, and video game titles like No Man’s Sky and Horizon Zero Dawn taking advantage ofPCG, the gap between academia and industry is as wide as it has ever been, in terms of communication and sharing methods. One of the authors, has worked on both sides of this gap and in an effort to shorten it and increase the synergy between the two sectors, has identified three design pillars for PCG using mixed-initiative interfaces. The three pillars are Respect Designer Control, Respect the Creative Process and Respect Existing Work Processes. Respecting designer control is about creating a tool that gives enough control to bring out the designer’s vision. Respecting the creative process concerns itself with having a feedback loop that is short enough, that the creative process is not disturbed. Respecting existing work processes means that a PCG tool should plug in easily to existing asset pipelines. As academics and communicators, it is surprising that publications often do not describe ways for developers to use our work or lack considerations for how a piece ofwork might fit into existing content pipelines
Evaluating Mixed-Initiative Procedural Level Design Tools using a Triple-Blind Mixed-Method User Study
Results from a triple-blind mixed-method user study into the effectiveness of mixed-initiative tools for the procedural generation of game levels are presented. A tool which generates levels using interactive evolutionary optimisation was designed for this study which (a) is focused on supporting the designer to explore the design space and (b) only requires the designer to interact with it by designing levels. The tool identifies level design patterns in an initial hand-designed map and uses that information to drive an interactive optimisation algorithm. A rigorous user study was designed which compared the experiences of designers using the mixed-initiative tool to designers who were given a tool which provided completely random level suggestions. The designers using the mixed-initiative tool showed an increased engagement in the level design task, reporting that it was effective in inspiring new ideas and design directions. This provides significant evidence that procedural content generation can be used as a powerful tool to support the human design process
Searching for good and diverse game levels
Abstract: In procedural content generation, one is often interested in generating a large number of artifacts that are not only of high quality but also diverse, in terms of gameplay, visual impression or some other criterion. We investigate several search-based approaches to creating good and diverse game content, in particular approaches based on evolution strategies with or without diversity preservation mechanisms, novelty search and random search. The content domain is game levels, more precisely map sketches for strategy games, which are meant to be used as suggestions in the Sentient Sketchbook design tool. Several diversity metrics are possible for this type of content: we investigate tile-based, objective-based and visual impression distance. We find that evolution with diversity preservation mechanisms can produce both good and diverse content, but only when using appropriate distance measures. Reversely, we can draw conclusions about the suitability of these distance measures for the domain from the comparison of diversity preserving versus blind restart evolutionary algorithms.peer-reviewe
Orchestrating Game Generation
The design process is often characterized by and realized through the iterative steps of evaluation and refinement. When the process is based on a single creative domain such as visual art or audio production, designers primarily take inspiration from work within their domain and refine it based on their own intuitions or feedback from an audience of experts from within the same domain. What happens, however, when the creative process involves more than one creative domain such as in a digital game? How should the different domains influence each other so that the final outcome achieves a harmonized and fruitful communication across domains? How can a computational process orchestrate the various computational creators of the corresponding domains so that the final game has the desired functional and aesthetic characteristics? To address these questions, this article identifies game facet orchestration as the central challenge for AI-based game generation, discusses its dimensions and reviews research in automated game generation that has aimed to tackle it. In particular, we identify the different creative facets of games, we propose how orchestration can be facilitated in a top-down or bottom-up fashion, we review indicative preliminary examples of orchestration, and we conclude by discussing the open questions and challenges ahead
The Right Variety: Improving Expressive Range Analysis with Metric Selection Methods
Expressive Range Analysis (ERA), an approach for visualising the output of
Procedural Content Generation (PCG) systems, is widely used within PCG research
to evaluate and compare generators, often to make comparative statements about
their relative performance in terms of output diversity and search space
exploration. Producing a standard ERA visualisation requires the selection of
two metrics which can be calculated for all generated artefacts to be
visualised. However, to our knowledge there are no methodologies or heuristics
for justifying the selection of a specific metric pair over alternatives. Prior
work has typically either made a selection based on established but unjustified
norms, designer intuition, or has produced multiple visualisations across all
possible pairs. This work aims to contribute to this area by identifying
valuable characteristics of metric pairings, and by demonstrating that pairings
that have these characteristics have an increased probability of producing an
informative ERA projection of the underlying generator. We introduce and
investigate three quantifiable selection criteria for assessing metric pairs,
and demonstrate how these criteria can be operationalized to rank those
available. Though this is an early exploration of the concept of quantifying
the utility of ERA metric pairs, we argue that the approach explored in this
paper can make ERA more useful and usable for both researchers and game
designers.Comment: To be published in the Proceedings of 18th International Conference
on the Foundations of Digital Games, and presented at the associated
conference in Lisbon, April 2023. 11 pages, 6 figures, 3 table
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