Name
Abstract | ||
|---|---|---|
A key challenge of procedural content generation (PCG) is to evoke a certain player experience (PX), when we have no direct control over the content which gives rise to that experience. We argue that neither the rigorous methods to assess PX in HCI, nor specialised methods in PCG are sufficient, because they rely on a human in the loop. We propose to address this shortcoming by means of computational models of intrinsic motivation and AI game-playing agents. We hypothesise that our approach could be used to automatically predict PX across games and content types without relying on a human player or designer. We conduct an exploratory study in level generation based on empowerment, a specific model of intrinsic motivation. Based on a thematic analysis, we find that empowerment can be used to create levels with qualitatively different PX. We relate the identified experiences to established theories of PX in HCI and game design, and discuss next steps. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3116595.3116631 | CHI PLAY'17: PROCEEDINGS OF THE ANNUAL SYMPOSIUM ON COMPUTER-HUMAN INTERACTION IN PLAY |
Keywords | Field | DocType |
Player Experience, Procedural Content Generation, Models of Intrinsic Motivation, AI Players, Empowerment | Thematic analysis,Content generation,Simulation,Computer science,Game design,Computational model,Human-in-the-loop,Multimedia,Exploratory research,Player experience,Empowerment | Conference |
Citations | PageRank | References |
5 | 0.46 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Guckelsberger | 1 | 34 | 9.77 |
| Christoph Salge | 2 | 61 | 15.75 |
| Jeremy Gow | 3 | 1702 | 84.56 |
| Paul A. Cairns | 4 | 259 | 31.54 |