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Exploring the Dynamic Properties of Interaction in Mixed-Initiative Procedural Content Generation
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-7738-1601
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 [en]
Mixed-Initiative, Procedural Content Generation, Quality Diversity, Computer Games, Evolutionary Algorithms
National Category
Computer Sciences Human Computer Interaction
Identifiers
URN: urn:nbn:se:mau:diva-18358DOI: 10.24834/isbn.9789178771400ISBN: 978-91-7877-139-4 (print)ISBN: 978-91-7877-140-0 (electronic)OAI: oai:DiVA.org:mau-18358DiVA, id: diva2:1471182
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
List of papers
1. Fostering Creativity in the Mixed-Initiative Evolutionary Dungeon Designer
Open this publication in new window or tab >>Fostering Creativity in the Mixed-Initiative Evolutionary Dungeon Designer
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2018 (English)In: Proceedings of the 13th International Conference on the Foundations of Digital Games, ACM Digital Library, 2018, article id 50Conference paper, Published paper (Refereed)
Abstract [en]

Mixed-initiative systems highlight the collaboration between humans and computers in fostering the generation of more interesting content in game design. In light of the ever-increasing cost of game development, providing mixed-initiative tools can not only significantly reduce the cost but also encourage more creativity amongst game designers. The Evolutionary Dungeon Designer (EDD) [3] is a mixed-initiative tool with a focus on using evolutionary computation to procedurally generate content that adhere to game design patterns. As part of an ongoing project, feedback from a user study on EDD's capabilities as a mixed-initiative design tool pointed out the need for improvement on the tool's functionalities [4]. In this paper we present a review of the principles of the mixed-initiative model, as well as the existing approaches that implement it. The outcome of this analysis allows us to address the appointed needs for improvement by shaping a new version of EDD that we describe here. Finally, we also present the results from a user study carried out with professional game developers, in order to assess EDD's new functionalities. Results show an overall positive evaluation of the tool's intuitiveness and capabilities for empowering game developers' creative skills during the design process of dungeons for adventure games. They also allow us to identify upcoming challenges pattern-based mixed-initiative tools could benefit from.

Place, publisher, year, edition, pages
ACM Digital Library, 2018
Keywords
Mixed-Initiative Design, Procedural Content Generation, Game Design Patterns
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mau:diva-16793 (URN)10.1145/3235765.3235815 (DOI)2-s2.0-85055514392 (Scopus ID)27339 (Local ID)27339 (Archive number)27339 (OAI)
Conference
International Conference on the Foundations of Digital Games, Malmö, Sweden (2018, August 7-10)
Available from: 2020-03-30 Created: 2020-03-30 Last updated: 2024-04-04Bibliographically approved
2. Assessing Aesthetic Criteria in the Evolutionary dungeon Designer
Open this publication in new window or tab >>Assessing Aesthetic Criteria in the Evolutionary dungeon Designer
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2018 (English)In: Proceedings of the 13th International Conference on the Foundations of Digital Games, ACM Digital Library, 2018, article id 44Conference paper, Published paper (Refereed)
Abstract [en]

The Evolutionary Dungeon Designer (EDD) [1] is as a mixed-initiative tool for creating dungeons for adventure games. Results from a user study with game developers positively evaluated EDD as a suitable framework for collaboration between human designers and PCG suggestions, highlighting these as time-saving and inspiring for creating dungeons [2]. Previous work on EDD identified the need of assessing aesthetic criteria as a key area for improvement in its PCG Engine. By upgrading the individual encoding system and the fitness evaluation in EDD's evolutionary algorithm, we present three techniques to preserve and account the designer's aesthetic criteria during the dungeon generation process: the capability of locking sections for preserving custom aesthetic structures, as well as the measurement of symmetry and similarity in the provided suggestions.

Place, publisher, year, edition, pages
ACM Digital Library, 2018
Series
International Conference Proceeding Series (ICPS)
Keywords
Procedural Content Generation, Evolutionary Algorithms, Mixed-Initiative Design
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mau:diva-16819 (URN)10.1145/3235765.3235810 (DOI)2-s2.0-85055581467 (Scopus ID)27341 (Local ID)27341 (Archive number)27341 (OAI)
Conference
International Conference on the Foundations of Digital Games, Malmö, Sweden (2018, August 7-10)
Available from: 2020-03-30 Created: 2020-03-30 Last updated: 2024-02-05Bibliographically approved
3. Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites
Open this publication in new window or tab >>Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites
2019 (English)Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2019
Series
IEEE Conference on Computational Intelligence and Games, ISSN 2325-4270, E-ISSN 2325-4289
Keywords
Procedural Content Generation, Evolutionary Algorithms, MIxed-Initiative Co-Creativity, Computer Games
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mau:diva-12640 (URN)10.1109/CIG.2019.8848022 (DOI)000843154300065 ()2-s2.0-85073116656 (Scopus ID)30472 (Local ID)30472 (Archive number)30472 (OAI)
Conference
IEEE Conference on Games, London, United Kingdom (2019/08/20 - 2019/08/23)
Available from: 2020-02-29 Created: 2020-02-29 Last updated: 2023-12-14Bibliographically approved
4. Perceived Behaviors of Personality-Driven Agents
Open this publication in new window or tab >>Perceived Behaviors of Personality-Driven Agents
2019 (English)In: Violence - Perception - Video Games: New Directions in Game Research / [ed] Federico Alvarez Igarzábal, Michael S. Debus, Curtis L. Maughan, Transcript Verlag, 2019, p. 171-184Chapter in book (Refereed)
Place, publisher, year, edition, pages
Transcript Verlag, 2019
Series
Bild und Bit : Studies of digital media culture, ISSN 2702-8240, E-ISSN 2702-8259
National Category
Human Computer Interaction Computer Sciences
Identifiers
urn:nbn:se:mau:diva-18277 (URN)10.14361/9783839450512-015 (DOI)2-s2.0-85124557944 (Scopus ID)9783839450512 (ISBN)
Available from: 2020-09-21 Created: 2020-09-21 Last updated: 2023-12-20Bibliographically approved
5. Learning the Designer’s Preferences to Drive Evolution
Open this publication in new window or tab >>Learning the Designer’s Preferences to Drive Evolution
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
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 12104
Keywords
Procedural Content Generation, Machine Learning, Mixed-initiative Co-Creativity, Evolutionary Computation
National Category
Human Computer Interaction Computer Sciences
Research subject
Interaktionsdesign
Identifiers
urn:nbn:se:mau:diva-18273 (URN)10.1007/978-3-030-43722-0_28 (DOI)000896394100028 ()2-s2.0-85084747634 (Scopus ID)978-3-030-43722-0 (ISBN)978-3-030-43721-3 (ISBN)
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
6. Toward Designer Modeling Through Design Style Clustering
Open this publication in new window or tab >>Toward Designer Modeling Through Design Style Clustering
2022 (English)In: IEEE Transactions on Games, ISSN 2475-1502, Vol. 14, no 4, p. 676-686Article in journal (Refereed) Published
Abstract [en]

We propose modeling designer style in mixed-initiative game content creation tools as archetypical design traces. These design traces are formulated as transitions between design styles; these design styles are in turn found through clustering all intermediate designs along the way to making a complete design. This method is implemented in the Evolutionary Dungeon Designer, a research platform for mixed-initiative systems to create adventure and dungeon crawler games. We present results both in the form of design styles for rooms, which can be analyzed to better understand the kind of rooms designed by users, and in the form of archetypical sequences between these rooms, i.e., Designer Personas.

Place, publisher, year, edition, pages
IEEE, 2022
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-51543 (URN)10.1109/tg.2022.3143800 (DOI)000908822400014 ()2-s2.0-85123365921 (Scopus ID)
Available from: 2022-05-19 Created: 2022-05-19 Last updated: 2024-02-05Bibliographically approved

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Alvarez, Alberto

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