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Learning the Designer’s Preferences to Drive Evolution
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-7738-1601
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0003-3924-7484
2020 (English)In: EvoApplications 2020: Applications of Evolutionary Computation, Springer, 2020, p. 431-445Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2020. p. 431-445
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 12104
Keywords [en]
Procedural Content Generation, Machine Learning, Mixed-initiative Co-Creativity, Evolutionary Computation
National Category
Human Computer Interaction Computer Sciences
Research subject
Interaktionsdesign
Identifiers
URN: urn:nbn:se:mau:diva-18273DOI: 10.1007/978-3-030-43722-0_28ISI: 000896394100028Scopus ID: 2-s2.0-85084747634ISBN: 978-3-030-43722-0 (electronic)ISBN: 978-3-030-43721-3 (print)OAI: oai:DiVA.org:mau-18273DiVA, id: diva2:1469157
Conference
Applications of Evolutionary Computation, 15-17 April 2020, Seville, Spain
Available from: 2020-09-21 Created: 2020-09-21 Last updated: 2023-12-14Bibliographically approved
In thesis
1. Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation
Open this publication in new window or tab >>Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation
2020 (English)Licentiate thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Malmö: Malmö universitet, 2020. p. 237
Series
Studies in Computer Science
Keywords
Mixed-Initiative, Procedural Content Generation, Quality Diversity, Computer Games, Evolutionary Algorithms
National Category
Computer Sciences Human Computer Interaction
Identifiers
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 (English)
Opponent
Supervisors
Available from: 2020-09-28 Created: 2020-09-28 Last updated: 2024-02-23Bibliographically approved
2. Exploring Game Design through Human-AI Collaboration
Open this publication in new window or tab >>Exploring Game Design through Human-AI Collaboration
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Malmö: Malmö universitet, 2022. p. 381
Series
Studies in Computer Science ; 20
Keywords
Computer Games, Human-AI Collaboration, Mixed-Initiative, Procedural Content Generation, Quality Diversity, Computational Creativity
National Category
Computer Sciences Human Computer Interaction
Research subject
Interaktionsdesign
Identifiers
urn:nbn:se:mau:diva-54586 (URN)10.24834/isbn.9789178773084 (DOI)978-91-7877-307-7 (ISBN)978-91-7877-308-4 (ISBN)
Public defence
2022-09-27, Niagara hörsal C, Nordenskiöldsgatan 1, 21119, Malmö, 14:30 (English)
Opponent
Supervisors
Available from: 2022-08-29 Created: 2022-08-27 Last updated: 2022-12-08Bibliographically approved

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Alvarez, AlbertoFont, Jose

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