Name
Abstract | ||
|---|---|---|
We examine the impact of strategy and dexterity on video games in which a player must use strategy to decide between multiple moves and must use dexterity to correctly execute those moves. We run simulation experiments on variants of two popular, interactive puzzle games: Tetris, which exhibits dexterity in the form of speed-accuracy time pressure, and Puzzle Bobble, which requires precise aiming. By modeling dexterity and strategy as separate components, we quantify the effect of each type of difficulty using normalized mean score and artificial intelligence agents that make human-like errors. We show how these techniques can model and visualize dexterity and strategy requirements as well as the effect of scoring systems on expressive range. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CIG.2017.8080427 | 2017 IEEE Conference on Computational Intelligence and Games (CIG) |
Keywords | Field | DocType |
AI-assisted game design,dexterity,strategy,difficulty,automated play testing | Computational intelligence,Computer science,Simulation,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-3234-5 | 1 | 0.38 |
References | Authors | |
24 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aaron Isaksen | 1 | 58 | 5.94 |
| Drew Wallace | 2 | 1 | 0.38 |
| Adam Finkelstein | 3 | 4041 | 299.42 |
| andrew nealen | 4 | 1175 | 53.78 |