11,118 research outputs found
Neuroevolutionary constrained optimization for content creation
This paper presents a constraint-based procedural
content generation (PCG) framework used for the creation of
novel and high-performing content. Specifically, we examine
the efficiency of the framework for the creation of spaceship
design (hull shape and spaceship attributes such as weapon and
thruster types and topologies) independently of game physics
and steering strategies. According to the proposed framework,
the designer picks a set of requirements for the spaceship
that a constrained optimizer attempts to satisfy. The constraint
satisfaction approach followed is based on neuroevolution;
Compositional Pattern-Producing Networks (CPPNs) which
represent the spaceship’s design are trained via a constraintbased
evolutionary algorithm. Results obtained in a number
of evolutionary runs using a set of constraints and objectives
show that the generated spaceships perform well in movement,
combat and survival tasks and are also visually appealing.peer-reviewe
The riddle of togelby
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.At the 2017 Artificial and Computational Intelligence in Games meeting at Dagstuhl, Julian Togelius asked how to make spaces where every way of filling in the details yielded a good game. This study examines the possibility of enriching search spaces so that they contain very high rates of interesting objects, specifically game elements. While we do not answer the full challenge of finding good games throughout the space, this study highlights a number of potential avenues. These include naturally rich spaces, a simple technique for modifying a representation to search only rich parts of a larger search space, and representations that are highly expressive and so exhibit highly restricted and consequently enriched search spaces. We treat the creation of plausible road systems, useful graphics, highly expressive room placement for maps, generation of cavern-like maps, and combinatorial puzzle spaces.Final Accepted Versio
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
Generative Design in Minecraft (GDMC), Settlement Generation Competition
This paper introduces the settlement generation competition for Minecraft,
the first part of the Generative Design in Minecraft challenge. The settlement
generation competition is about creating Artificial Intelligence (AI) agents
that can produce functional, aesthetically appealing and believable settlements
adapted to a given Minecraft map - ideally at a level that can compete with
human created designs. The aim of the competition is to advance procedural
content generation for games, especially in overcoming the challenges of
adaptive and holistic PCG. The paper introduces the technical details of the
challenge, but mostly focuses on what challenges this competition provides and
why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018
proceedings, as part of the workshop on Procedural Content Generatio
Creating Space: Building Digital Games
Studies of games, rhetoric, and pedagogy are increasingly common in our field, and indeed seem to grow each year. Nonetheless, composing and designing digital games, either as a mode of scholarship or as a classroom assignment, has not seen an equal groundswell. This selection first provides a brief overview of the existing scholarship in gaming and pedagogy, much of which currently focuses either on games as texts to analyze or as pedagogical models. While these approaches are certainly valuable, I advocate for an increased focus on game design and creation as valuable act of composition. Such a focus engages students and scholars in a deeply multimodal practice that incorporates critical design and computational thinking. I close with suggestions on tools for new and intrepid designers
A procedural procedural level generator generator
Procedural content generation (PCG) is concerned
with automatically generating game content, such as levels,
rules, textures and items. But could the content generator itself
be seen as content, and thus generated automatically? This
would be very useful if one wanted to avoid writing a content
generator for a new game, or if one wanted to create a content
generator that generates an arbitrary amount of content with a
particular style or theme. In this paper, we present a procedural
procedural level generator generator for Super Mario Bros.
It is an interactive evolutionary algorithm that evolves agent based level generators. The human user makes the aesthetic
judgment on what generators to prefer, based on several views
of the generated levels including a possibility to play them, and
a simulation-based estimate of the playability of the levels. We
investigate the characteristics of the generated levels, and to
what extent there is similarity or dissimilarity between levels
and between generators.peer-reviewe
Spatial Transfiguration: Anamorphic Mixed-Reality in the Virtual Reality Panorama
Spatial illusion and immersion was achieved in Renaissance painting through the manipulation of linear perspective’s pictorial conventions and painterly technique. The perceptual success of a painted trompe l’œil, its ability to fool the observer into believing they were viewing a real three-dimensional scene, was constrained by the limited immersive capacity of the two-dimensional painted canvas. During the baroque period however, artists began to experiment with the amalgamation of the ‘real’ space occupied by the observer together with the pictorial space enveloped by the painting’s picture plane: real and pictorial space combined into one pictorial composition resulting in a hybridised ‘mixed-reality’. Today, the way architects, and designers generally, use the QuickTime Virtual Reality panorama to represent spaces of increasing visual density have much to learn from the way in which Renaissance and baroque artists manipulated the three-dimensional characteristics of the picture plane in order to offer more convincing spatial illusions. This paper outlines the conceptual development of the QuickTime VR panorama by Ken Turkowski and the Apple Advanced Technology Group during the late 1980s. Further, it charts the technical methods of the Virtual Reality panorama’s creation in order to reflect upon the VR panorama’s geometric construction and range and effectiveness of spatial illusion. Finally, through a brief analysis of Hans Holbein’s Ambassadors [1533] and Andrea Pozzo’s nave painting in Sant ‘Ignazio [1691-94] this paper proposes an alternative conceptual model for the pictorial construction of the VR panorama that is innovatively based upon an anamorphic ‘mixed-reality’
The Case for a Mixed-Initiative Collaborative Neuroevolution Approach
It is clear that the current attempts at using algorithms to create
artificial neural networks have had mixed success at best when it comes to
creating large networks and/or complex behavior. This should not be unexpected,
as creating an artificial brain is essentially a design problem. Human design
ingenuity still surpasses computational design for most tasks in most domains,
including architecture, game design, and authoring literary fiction. This leads
us to ask which the best way is to combine human and machine design capacities
when it comes to designing artificial brains. Both of them have their strengths
and weaknesses; for example, humans are much too slow to manually specify
thousands of neurons, let alone the billions of neurons that go into a human
brain, but on the other hand they can rely on a vast repository of common-sense
understanding and design heuristics that can help them perform a much better
guided search in design space than an algorithm. Therefore, in this paper we
argue for a mixed-initiative approach for collaborative online brain building
and present first results towards this goal.Comment: Presented at WebAL-1: Workshop on Artificial Life and the Web 2014
(arXiv:1406.2507
- …