Title:
Machine Learning based Procedural Content Generation in Semantic Choreography
Machine Learning based Procedural Content Generation in Semantic Choreography
| dc.contributor.author | Xiao, Kyle Phillip | |
| dc.contributor.committeeMember | Riedl, Mark | |
| dc.contributor.committeeMember | Magerko, Brian | |
| dc.contributor.department | Interactive Computing | |
| dc.date.accessioned | 2020-11-09T16:58:53Z | |
| dc.date.available | 2020-11-09T16:58:53Z | |
| dc.date.created | 2020-05 | |
| dc.date.issued | 2020-05 | |
| dc.date.submitted | May 2020 | |
| dc.date.updated | 2020-11-09T16:58:53Z | |
| dc.description.abstract | BeatMania is a rhythm-action game where players press buttons in response to keysound events to generate music. Rhythm-action game charts (the sequence of keysound events) have traditionally been human authored, since each song level must be creatively organized and correspond an overall pattern or theme. A deep neural network approach is proposed for rhythm-action game chart creation, and a method of level evaluation for co-creative AI is defined. That is, given an arbitrary piece of music, human users can generate BeatMania charts as well as give input to an AI collaborator. The problem is divided into two parts: autonomous chart generation and design interaction. For the chart generation process, a combination of features that include grouping information and audio sample labels are incorporated into an artificial neural network. For the design interaction, principal component analysis is utilized for a proposed reinforcement learning model. The co-creative tool is tested against Markov Chain and LSTM baselines via human trials. | |
| dc.description.degree | Undergraduate | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/63840 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Machine Learning | |
| dc.subject | Procedural Content Generation | |
| dc.subject | Deep Learning | |
| dc.subject | Co-Creativity | |
| dc.subject | Generative Models | |
| dc.subject | Artificial Intelligence | |
| dc.subject | LSTM | |
| dc.subject | CNN | |
| dc.subject | GAN | |
| dc.subject | Game AI | |
| dc.subject | Rhythm-Action Game | |
| dc.title | Machine Learning based Procedural Content Generation in Semantic Choreography | |
| dc.type | Text | |
| dc.type.genre | Undergraduate Thesis | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | College of Computing | |
| local.contributor.corporatename | School of Interactive Computing | |
| local.contributor.corporatename | Undergraduate Research Opportunities Program | |
| local.relation.ispartofseries | Undergraduate Research Option Theses | |
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| thesis.degree.level | Undergraduate |