Publikationer från Malmö universitet
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
TropeTwist:Trope-based Narrative Structure Generation
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: 0000-0003-3924-7484
2022 (engelsk)Inngår i: Proceedings of the 13th Workshop on Procedural Content Generation, FDG, Association for Computing Machinery (ACM), 2022Konferansepaper, Oral presentation with published abstract (Fagfellevurdert)
Abstract [en]

Games are complex, multi-faceted systems that share common elements and underlying narratives, such as the conflict between a hero and a big bad enemy or pursuing a goal that requires overcoming challenges. However, identifying and describing these elements together is non-trivial as they might differ in certain properties and how players might encounter the narratives. Likewise, generating narratives also pose difficulties when encoding, interpreting, and evaluating them. To address this, we present TropeTwist, a trope-based system that can describe narrative structures in games in a more abstract and generic level, allowing the definition of games' narrative structures and their generation using interconnected tropes, called narrative graphs. To demonstrate the system, we represent the narrative structure of three different games. We use MAP-Elites to generate and evaluate novel quality-diverse narrative graphs encoded as graph grammars, using these three hand-made narrative structures as targets. Both hand-made and generated narrative graphs are evaluated based on their coherence and interestingness, which are improved through evolution.

sted, utgiver, år, opplag, sider
Association for Computing Machinery (ACM), 2022.
Emneord [en]
Authoring Tools, Narrative Generation, Evolutionary Computation, MAP-Elites, Computer Games
HSV kategori
Forskningsprogram
Interaktionsdesign
Identifikatorer
URN: urn:nbn:se:mau:diva-54331OAI: oai:DiVA.org:mau-54331DiVA, id: diva2:1685873
Konferanse
The International Conference on the Foundations of Digital Games (FDG)
Tilgjengelig fra: 2022-08-05 Laget: 2022-08-05 Sist oppdatert: 2022-12-07bibliografisk kontrollert
Inngår i avhandling
1. Exploring Game Design through Human-AI Collaboration
Åpne denne publikasjonen i ny fane eller vindu >>Exploring Game Design through Human-AI Collaboration
2022 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Malmö: Malmö universitet, 2022. s. 381
Serie
Studies in Computer Science ; 20
Emneord
Computer Games, Human-AI Collaboration, Mixed-Initiative, Procedural Content Generation, Quality Diversity, Computational Creativity
HSV kategori
Forskningsprogram
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)
Disputas
2022-09-27, Niagara hörsal C, Nordenskiöldsgatan 1, 21119, Malmö, 14:30 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2022-08-29 Laget: 2022-08-27 Sist oppdatert: 2022-12-08bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Person

Alvarez, AlbertoFont, Jose

Søk i DiVA

Av forfatter/redaktør
Alvarez, AlbertoFont, Jose
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric

urn-nbn
Totalt: 52 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf