Show simple item record

dc.contributor.advisorConnor, Andy
dc.contributor.advisorMarks, Stefan
dc.contributor.authorKruse, Jan
dc.date.accessioned2019-10-30T22:46:27Z
dc.date.available2019-10-30T22:46:27Z
dc.date.copyright2019
dc.identifier.urihttp://hdl.handle.net/10292/12944
dc.description.abstractCreating computer game levels that offer good playability and interesting layouts is a laborious and costly task. In particular, multiplayer game levels necessitate careful balancing of gameplay between teams and clear objectives for players. Expert knowledge that draws on a good understanding of player experience and strong level design skills results in maps that players enjoy and appreciate. The success of new level designs hinges on this expertise. This study introduces a Multi-Agent System based on heuristics developed from expert level designers, that is able to augment game designers of all levels of expertise and help them create First Person Shooter levels with good playability. An interactive evolutionary algorithm is employed to provide designers with several level design options. The human user stays in full control of the decisions made during the design process, while the agent system provides suggestions that promise good playability and a positive player experience. The system has been evaluated using game designers with varying levels of experience, and the results show great promise. A Multi-Agent System in addition to the Interactive Genetic Algorithm outperforms a purely human-centric solution: Game designers of all skill levels draw heavily on the suggestions made by the Multi-Agent System. Recommendations for future developments are given.en_NZ
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.subjectGame Designen_NZ
dc.subjectProcedural Content Generationen_NZ
dc.subjectEvolutionary Computaitonen_NZ
dc.subjectGenetic Algorithmsen_NZ
dc.subjectAutonomous Agentsen_NZ
dc.subjectCognitive Modelsen_NZ
dc.subjectMachine Learningen_NZ
dc.titleDesigner-driven Procedural Game Content Generation using Multi-agent Evolutionary Computationen_NZ
dc.typeThesisen_NZ
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelDoctoral Theses
thesis.degree.nameDoctor of Philosophyen_NZ
dc.rights.accessrightsOpenAccess
dc.date.updated2019-10-30T03:25:36Z


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record