39,071 research outputs found
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
Evolving Aesthetic Maps for a Real Time Strategy Game
ArtÃculo publicado en congreso SEED'2013 (I Spanish Symposium on Entertainment Computing), Septiembre 2013, Madrid.This paper presents a procedural content generator method that have
been able to generate aesthetic maps for a real-time strategy game. The
maps has been characterized based on several of their properties in order
to de ne a similarity function between scenarios. This function has guided
a multi-objective evolution strategy during the process of generating and
evolving scenarios that are similar to other aesthetic maps while being
di erent to a set of non-aesthetic scenarios. The solutions have been
checked using a support-vector machine classi er and a self-organizing
map obtaining successful results (generated maps have been classi ed as
aesthetic maps)
AI Researchers, Video Games Are Your Friends!
If you are an artificial intelligence researcher, you should look to video
games as ideal testbeds for the work you do. If you are a video game developer,
you should look to AI for the technology that makes completely new types of
games possible. This chapter lays out the case for both of these propositions.
It asks the question "what can video games do for AI", and discusses how in
particular general video game playing is the ideal testbed for artificial
general intelligence research. It then asks the question "what can AI do for
video games", and lays out a vision for what video games might look like if we
had significantly more advanced AI at our disposal. The chapter is based on my
keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad
audience.Comment: in Studies in Computational Intelligence Studies in Computational
Intelligence, Volume 669 2017. Springe
Procedural content generation of virtual terrain for games
Abstract. Game developers use Procedural Content Generation (PCG) in aid of game development to reduce costs, reach better memory consumption, increase creativity, and augment our limited human imagination by generating content algorithmically. Virtual terrain is one of the main topics of PCG; how well do these techniques support the special needs of game level design? To answer this question, a literature review was conducted to analyse correlation between the capabilities of various PCG-techniques and the needs of level design patterns. We observed that techniques permitting higher degree of local control increased their applicability for virtual terrain in games and that traditional fractal techniques, such as the midpoint displacement method and noise-functions, performed poorly despite their popularity. Our foremost contributions to this field of study were new insights towards more suitable PCG-techniques for use in game development
Tile Pattern KL-Divergence for Analysing and Evolving Game Levels
This paper provides a detailed investigation of using the Kullback-Leibler
(KL) Divergence as a way to compare and analyse game-levels, and hence to use
the measure as the objective function of an evolutionary algorithm to evolve
new levels. We describe the benefits of its asymmetry for level analysis and
demonstrate how (not surprisingly) the quality of the results depends on the
features used. Here we use tile-patterns of various sizes as features.
When using the measure for evolution-based level generation, we demonstrate
that the choice of variation operator is critical in order to provide an
efficient search process, and introduce a novel convolutional mutation operator
to facilitate this. We compare the results with alternative generators,
including evolving in the latent space of generative adversarial networks, and
Wave Function Collapse. The results clearly show the proposed method to provide
competitive performance, providing reasonable quality results with very fast
training and reasonably fast generation.Comment: 8 pages plus references. Proceedings of GECCO 201
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