Taksim: A Constrained Graph Partitioning Framework for Procedural Content Generation

Abuzuraiq, Ahmed M.; Ferguson, Arron; Pasquier, Philippe

doi:10.1109/cig.2019.8848065

Cited by 2 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, choosing and associating level design and narrative as two facets to explore within the holistic PCG approach is appropriated. This was presented and described by Kybartas and Bidarra's survey [195], and explored and supported by related work [193,[196][197][198][199][200]. Within the narrative facet and in most games, quests are an essential component.…”

Section: Holistic Procedural Content Generationmentioning

confidence: 98%

Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation

Alvarez¹

View full text Add to dashboard Cite

As AI develops, grows, and expands, the more benefits we can have from it. AI is used in multiple fields to assist humans, such as object recognition, self-driving cars, or design tools. However, AI could be used for more than assisting humans in their tasks. It could be employed to collaborate with humans as colleagues in shared tasks, which is usually described as Mixed-Initiative (MI) paradigm. This paradigm creates an interactive scenario that leverage on AI and human strengths with an alternating and proactive initiative to approach a task. However, this paradigm introduces several challenges. For instance, there must be an understanding between humans and AI, where autonomy and initiative become negotiation tokens. In addition, control and expressiveness need to be taken into account to reach some goals. Moreover, although this paradigm has a broader application, it is especially interesting for creative tasks such as games, which are mainly created in collaboration. Creating games and their content is a hard and complex task, since games are content-intensive, multi-faceted, and interacted by external users. Therefore, this thesis explores MI collaboration between human game designers and AI for the co-creation of games, where the AI's role is that of a colleague with the designer. The main hypothesis is that AI can be incorporated in systems as a collaborator, enhancing design tools, fostering human creativity, reducing their workload, and creating adaptive experiences. Furthermore, This collaboration arises several dynamic properties such as control, expressiveness, and initiative, which are all central to this thesis. Quality-Diversity algorithms combined with control mechanisms and interactions for the designer are proposed to investigate this collaboration and properties. Designer and Player modeling is also explored, and several approaches are proposed to create a better workflow, establish adaptive experiences, and enhance the interaction. Through this, it is demonstrated the potential and benefits of these algorithms and models in the MI paradigm.

show abstract

Section: Holistic Procedural Content Generationmentioning

confidence: 98%

Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation

Alvarez¹

View full text Add to dashboard Cite

show abstract

“…For instance, in [84] and [85], mechanics, graphics, and levels are generated together, although through different processes. Two facets that are commonly associated with each other are the narrative and level facet [64,[86][87][88]. This is due to the fact that space requires context to make sense of it, and narrative requires space to develop.…”

Section: Procedural Content Generationmentioning

confidence: 99%

E X P Loring Game De S Ign Through Human-Ai Col Laborat Ion

Alvarez¹

View full text Add to dashboard Cite

Game design is a hard and multi-faceted task that intertwines different gameplay mechanics, audio, level, graphic, and narrative facets. Games' facets are developed in conjunction with others with a common goal that makes games coherent and interesting. These combinations result in plenty of games in diverse genres, which usually require a collaboration of a diverse group of designers. Collaborators can take different roles and support each other with their strengths resulting in games with unique characteristics. The multi-faceted nature of games and their collaborative properties and requirements make it an exciting task to use Artificial Intelligence (AI). The generation of these facets together requires a holistic approach, which is one of the most challenging tasks within computational creativity. Given the collaborative aspect of games, this thesis approaches their generation through Human-AI collaboration, specifically using a mixed-initiative co-creative (MI-CC) paradigm. This paradigm creates an interactive and collaborative scenario that leverages AI and human strengths with an alternating and proactive initiative to approach a task. However, this paradigm introduces several challenges, such as Human and AI goal alignment or competing properties. In this thesis, game design and the generation of game facets by themselves and intertwined are explored through Human-AI collaboration. The AI takes a colleague's role with the designer, arising multiple dynamics, challenges, and opportunities. The main hypothesis is that AI can be incorporated into systems as a collaborator, enhancing design tools, fostering human creativity, and reducing workload. The challenges and opportunities that arise from this are explored, discussed, and approached throughout the thesis. As a result, multiple approaches and methods such as quality-diversity algorithms and designer modeling are proposed to generate game facets in tandem with humans, create a better workflow, enhance the interaction, and establish adaptive experiences.

show abstract

Taksim: A Constrained Graph Partitioning Framework for Procedural Content Generation

Cited by 2 publications

References 13 publications

Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation

Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation

E X P Loring Game De S Ign Through Human-Ai Col Laborat Ion

Contact Info

Product

Resources

About