From competitive to social two-player videogames
Proceedings of the 2nd Workshop on Child, Computer and Interaction
Effects of different scenarios of game difficulty on player immersion
Interacting with Computers
Dynamic game difficulty balancing for backgammon
Proceedings of the 49th Annual Southeast Regional Conference
Dynamic difficulty balancing for cautious players and risk takers
International Journal of Computer Games Technology
Game team balancing by using particle swarm optimization
Knowledge-Based Systems
An Efficient Gaming User Oriented Load Balancing Scheme for MMORPGs
Wireless Personal Communications: An International Journal
| Hi-index | 0.00 |
Dealing with users of different skills, and of variable capacity for learning and adapting over time, is a key issue in Human-Machine Interaction, particularly in highly interactive applications such as computer games. Indeed, a recognized major concern for the game developersý community is to provide mechanisms to dynamically balance the difficulty level of the games in order to keep the user interested in playing. This work presents an innovative use of reinforcement learning techniques to build intelligent agents that adapt their behavior in order to provide dynamic game balancing. The idea is to couple learning with an action selection mechanism which depends on the evaluation of the current userýs skills. To validate our approach, we applied it to a real-time fighting game, obtaining good results, as the adaptive agent is able to quickly play at the same level as opponents with different skills.