Name
Abstract | ||
|---|---|---|
Dynamic adaptation of games with the objective of catering to the individual players' level of standard is an emerging and challenging research area of artificial intelligence in digital game. In this paper, we propose a data-driven approach for dynamic adaptation of game scenario difficulties. The goal is to fit the performance of the player to the desired conditions set by the designer. To this end, the data on player's in-game performance and dynamic game states are utilized for making adaptation decisions. Trained artificial neural networks are used to capture the relationship between dynamic game state, player performance, adaptation decision and the resultant game difficulty. Based on the predicted difficulty, adaptation of both direction and magnitude can be performed more effectively. Experimental study on a training game application is presented to demonstrate the efficiency and stability of the proposed approach. |
Year | Venue | Field |
|---|---|---|
2015 | IEEE Conference on Computational Intelligence and Games | Data-driven,Game mechanics,Simulation,Computer science,Game design,Simulations and games in economics education,Artificial intelligence,Sequential game,Screening game,Non-cooperative game,Machine learning,Mathematical game |
DocType | ISSN | Citations |
Conference | 2325-4270 | 3 |
PageRank | References | Authors |
0.40 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiyan Yin | 1 | 54 | 5.71 |
| Linbo Luo | 2 | 53 | 7.54 |
| Wentong Cai | 3 | 1928 | 197.81 |
| Yew-Soon Ong | 4 | 4205 | 224.11 |
| Jing-hui Zhong | 5 | 380 | 33.00 |