Artificial intelligence agent for assessing game difficulty
| dc.contributor.advisor | Mirza-Babaei, Pejman | |
| dc.contributor.author | Sansalone, Stevie C. F. | |
| dc.date.accessioned | 2024-06-25T13:14:21Z | |
| dc.date.available | 2024-06-25T13:14:21Z | |
| dc.date.issued | 2024-04-01 | |
| dc.degree.discipline | Computer Science | |
| dc.degree.level | Master of Science (MSc) | |
| dc.description.abstract | Balancing game difficulty is a key element of developing games which engage players without causing frustration. Difficulty balancing is an iterative process and game developers frequently rely on user tests to guide them, but the cost of user testing can be prohibitive, particularly for independent developers. In response to this, I seek to determine whether AI can be used to test the difficulty of developing games before setting up a full playtest. For this purpose, I developed ARTemiS, a prototype tool for AI-based playtesting focused on game difficulty arising from precision input tasks such as combat. I then performed a user study with 10 participants who used the tool to assess and modify three demo levels. This research seeks to assess the applicability and utility of AI-based playtesting for difficulty balancing and lay a foundation for the development of tools for assessing other aspects of difficulty with AI. | |
| dc.description.sponsorship | University of Ontario Institute of Technology | |
| dc.identifier.uri | https://ontariotechu.scholaris.ca/handle/10155/1797 | |
| dc.language.iso | en | |
| dc.subject.other | Games user research | |
| dc.subject.other | Difficulty balancing | |
| dc.subject.other | Human-Computer Interaction | |
| dc.subject.other | Artificial Intelligence | |
| dc.subject.other | User testing | |
| dc.title | Artificial intelligence agent for assessing game difficulty | |
| dc.type | Thesis |