We propose a framework for automatic game parameter tuning using a game player model. Two kinds of computational intelligence techniques are used to create the framework: a fuzzy logic system (FS) as the decision maker and evolutionary computation as the model parameter optimizer. Insights from a game developer are integrated into the player model consisting of FS rules. FS membership function parameters are optimized by a differential evolution (DE) algorithm to find optimal model parameters. We conducted experiments in which our player model plays a turn-based strategy video game. DE optimization was able to evolve our player model such that it could compete well at various levels of game difficulty.
|Number of pages||11|
|Journal||International Journal of Innovative Computing, Information and Control|
|Publication status||Published - Dec 1 2017|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Information Systems
- Computational Theory and Mathematics