Abstract
Recent years, there has been growing interests in experience-driven procedural level generation. Various metrics have been formulated to model player experience and help generate personalised levels. In this work, we question whether experience metrics can adapt to agents with different personas. We start by reviewing existing metrics for evaluating game levels. Then, focusing on platformer games, we design a framework integrating various agents and evaluation metrics. Experimental studies on Super Mario Bros. indicate that using the same evaluation metrics but agents with different personas can generate levels for particular persona. It implies that, for simple games, using a game-playing agent of specific player archetype as a level tester is probably all we need to generate levels of diverse behaviour engagement.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2022 IEEE Conference on Games, CoG 2022 |
| Publisher | IEEE Computer Society |
| Pages | 167-174 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781665459891 |
| DOIs | |
| Publication status | Published - 2022 |
| Externally published | Yes |
| Event | 2022 IEEE Conference on Games, CoG 2022 - Beijing, China Duration: 21 Aug 2022 → 24 Aug 2022 |
Publication series
| Name | IEEE Conference on Computatonal Intelligence and Games, CIG |
|---|---|
| Volume | 2022-August |
| ISSN (Print) | 2325-4270 |
| ISSN (Electronic) | 2325-4289 |
Conference
| Conference | 2022 IEEE Conference on Games, CoG 2022 |
|---|---|
| Country/Territory | China |
| City | Beijing |
| Period | 21/08/22 → 24/08/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Experience-driven procedural content generation
- level generation
- personalised levels
- platformer games
- player experience