Ordering Levels in Human Computation Games using Playtraces and Level Structure
- Resource Type
- Conference
- Authors
- Sarkar, Anurag; Cooper, Seth
- Source
- 2022 IEEE Conference on Games (CoG) Games (CoG), 2022 IEEE Conference on. :620-623 Aug, 2022
- Subject
- Computing and Processing
Robotics and Control Systems
Annotations
Games
Manuals
dynamic difficulty adjustment
human computation games
rating systems
skill chains
playtrace
clustering
- Language
- ISSN
- 2325-4289
Prior work using skill chains for matchmaking-based dynamic difficulty adjustment in human computation games required skill chains to be manually defined for a game, and each level to be manually annotated with the individual skills needed to complete that level. In this work, we present two approaches for defining level orderings for DDA in the platformer HCG Iowa James without using such manually-defined skill chains and annotations. The first involves sequences of action-context pairs found in gameplay traces. The second consists of applying K-means clustering on segments of levels. Our results show that both new approaches outperform baseline random level ordering and perform similarly to the skill chain approach.