The AI Settlement Generation Challenge in Minecraft

Salge, Christoph; Green, Michael Cerny; Canaan, Rodrigo; Skwarski, Filip; Fritsch, Rafael; Brightmoore, Adrian; Ye, Shaofang; Cao, Changxing; Togelius, Julian

doi:10.1007/s13218-020-00635-0

Cited by 25 publications

(16 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We believe that this method has a large potential for generating urban environments in (indie) game contexts, including the Minecraft concept [25]. We are currently exploring its application to the generation of narrative-rich urban settlements [26].…”

Section: Discussionmentioning

confidence: 99%

Declarative procedural generation of architecture with semantic architectural profiles

Aanholt

Bidarra

2020

2020 IEEE Conference on Games (CoG)

View full text Add to dashboard Cite

Procedural content generation (PCG) for architecture is widely used in a variety of digital media, most notably in games. However, such methods are often limited in their expressive range, and require considerable technical knowledge to create non-trivial architectural structures. We present a novel tile-based PCG approach for generating architecture, that proposes the use of architectural profiles, a declarative characterization of architectural typology, within a generic tile solving framework. An architectural profile consists of a set of tiles, representing atomic architectural building blocks, and a set of declarative constraints and rules, specifying which conditions a tile configuration has to satisfy to be valid. These conditions are translated into logic constraints, and used by a tile solver to control tile placement in a bottom-up manner. Eventually, each valid model output by the solver is a representative instance of its architectural profile. We describe an implementation of this approach with Answer Set Programming, using an off-theshelf constraint solver for model generation. We performed an expressive range analysis, and concluded that our declarative method is quite controllable and can be steered over a broad range of architectural structures, regarding density and repetitiveness. Due to this expressive range and control, our tile-based method is very suitable for the customized development of urban environments for games.

show abstract

Section: Discussionmentioning

confidence: 99%

Declarative procedural generation of architecture with semantic architectural profiles

Aanholt

Bidarra

2020

2020 IEEE Conference on Games (CoG)

View full text Add to dashboard Cite

show abstract

“…Prior to 2021, MarL Ö competition (Perez-Liebana et al, 2019) challenged participants to solve multiple tasks, some unseen, in a multi-agent setting in Minecraft. The Generative Design in Minecraft competition (Salge et al, 2020) challenges participants to generate settlements that fit into the surrounding environment.…”

Section: Related Workmentioning

confidence: 99%

MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

Kanervisto¹,

Milani²,

Ramanauskas³

et al. 2022

Preprint

View full text Add to dashboard Cite

Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more broadly applicable methods, organizers need to enforce the use of general techniques, the use of sample-efficient methods, and the reproducibility of the results. While beneficial for the research community, these restrictions come at a cost-increased difficulty. If the barrier for entry is too high, many potential participants are demoralized. With this in mind, we hosted the third edition of the MineRL ObtainDiamond competition, MineRL Diamond 2021, with a separate track in which we permitted any solution to promote the participation of newcomers. With this track and more extensive tutorials and support, we saw an increased number of submissions. The participants of this easier track were able to obtain a diamond, and the participants of the harder track progressed the generalizable solutions in the same task.

show abstract

“…The complex 3D structures in this game pose a problem for PCGML methods. The AI Settlement Generation Challenge [15]- [17] was recently created to spur research in this direction. The submitted algorithms are generating villages in a given world and are evaluated using subjective measures (adaptability, functionality, narrative, aesthetics).…”

Section: Related Workmentioning

confidence: 99%

World-GAN: a Generative Model for Minecraft Worlds

Awiszus

Schubert

Rosenhahn

2021

2021 IEEE Conference on Games (CoG)

View full text Add to dashboard Cite

This work introduces World-GAN, the first method to perform data-driven Procedural Content Generation via Machine Learning in Minecraft from a single example. Based on a 3D Generative Adversarial Network (GAN) architecture, we are able to create arbitrarily sized world snippets from a given sample. We evaluate our approach on creations from the community as well as structures generated with the Minecraft World Generator. Our method is motivated by the dense representations used in Natural Language Processing (NLP) introduced with word2vec [1]. The proposed block2vec representations make World-GAN independent from the number of different blocks, which can vary a lot in Minecraft, and enable the generation of larger levels. Finally, we demonstrate that changing this new representation space allows us to change the generated style of an already trained generator. World-GAN enables its users to generate Minecraft worlds based on parts of their creations.

show abstract

The AI Settlement Generation Challenge in Minecraft

Cited by 25 publications

References 35 publications

Declarative procedural generation of architecture with semantic architectural profiles

Declarative procedural generation of architecture with semantic architectural profiles

MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

World-GAN: a Generative Model for Minecraft Worlds

Contact Info

Product

Resources

About