Publikationer från Malmö universitet
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Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0002-7738-1601
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0002-2644-2785
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0003-3924-7484
2019 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

We propose the use of quality-diversity algorithms for mixed-initiative game content generation. This idea is implemented as a new feature of the Evolutionary Dungeon Designer, a system for mixed-initiative design of the type of levels you typically find in computer role playing games. The feature uses the MAP-Elites algorithm, an illumination algorithm which divides the population into a number of cells depending on their values along several behavioral dimensions. Users can flexibly and dynamically choose relevant dimensions of variation, and incorporate suggestions produced by the algorithm in their map designs. At the same time, any modifications performed by the human feed back into MAP-Elites, and are used to generate further suggestions.

Ort, förlag, år, upplaga, sidor
IEEE, 2019.
Serie
IEEE Conference on Computational Intelligence and Games, ISSN 2325-4270, E-ISSN 2325-4289
Nyckelord [en]
Procedural Content Generation, Evolutionary Algorithms, MIxed-Initiative Co-Creativity, Computer Games
Nationell ämneskategori
Teknik och teknologier
Identifikatorer
URN: urn:nbn:se:mau:diva-12640DOI: 10.1109/CIG.2019.8848022ISI: 000843154300065Scopus ID: 2-s2.0-85073116656Lokalt ID: 30472OAI: oai:DiVA.org:mau-12640DiVA, id: diva2:1409687
Konferens
IEEE Conference on Games, London, United Kingdom (2019/08/20 - 2019/08/23)
Tillgänglig från: 2020-02-29 Skapad: 2020-02-29 Senast uppdaterad: 2023-12-14Bibliografiskt granskad
Ingår i avhandling
1. Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation
Öppna denna publikation i ny flik eller fönster >>Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation
2020 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Malmö: Malmö universitet, 2020. s. 237
Serie
Studies in Computer Science
Nyckelord
Mixed-Initiative, Procedural Content Generation, Quality Diversity, Computer Games, Evolutionary Algorithms
Nationell ämneskategori
Datavetenskap (datalogi) Människa-datorinteraktion (interaktionsdesign)
Identifikatorer
urn:nbn:se:mau:diva-18358 (URN)10.24834/isbn.9789178771400 (DOI)978-91-7877-139-4 (ISBN)978-91-7877-140-0 (ISBN)
Presentation
2020-11-20, OR:D138, Nordenskiöldsgatan 10, Malmö, 13:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2020-09-28 Skapad: 2020-09-28 Senast uppdaterad: 2024-02-23Bibliografiskt granskad
2. Exploring Game Design through Human-AI Collaboration
Öppna denna publikation i ny flik eller fönster >>Exploring Game Design through Human-AI Collaboration
2022 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Malmö: Malmö universitet, 2022. s. 381
Serie
Studies in Computer Science ; 20
Nyckelord
Computer Games, Human-AI Collaboration, Mixed-Initiative, Procedural Content Generation, Quality Diversity, Computational Creativity
Nationell ämneskategori
Datavetenskap (datalogi) Människa-datorinteraktion (interaktionsdesign)
Forskningsämne
Interaktionsdesign
Identifikatorer
urn:nbn:se:mau:diva-54586 (URN)10.24834/isbn.9789178773084 (DOI)978-91-7877-307-7 (ISBN)978-91-7877-308-4 (ISBN)
Disputation
2022-09-27, Niagara hörsal C, Nordenskiöldsgatan 1, 21119, Malmö, 14:30 (Engelska)
Opponent
Handledare
Tillgänglig från: 2022-08-29 Skapad: 2022-08-27 Senast uppdaterad: 2022-12-08Bibliografiskt granskad

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Alvarez, AlbertoDahlskog, SteveFont, Jose

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