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Perceived Behaviors of Personality-Driven Agents
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-7738-1601
IT University of Copenhagen.
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. p. 171-184
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: urn:nbn:se:mau:diva-18277DOI: 10.14361/9783839450512-015Scopus ID: 2-s2.0-85124557944ISBN: 9783839450512 (electronic)OAI: oai:DiVA.org:mau-18277DiVA, id: diva2:1469182
Available from: 2020-09-21 Created: 2020-09-21 Last updated: 2023-12-20Bibliographically 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

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Alvarez, Alberto

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