Procedural Content Generation for GDL Descriptions of Simplified Boardgames
This work aims to diversify and extend the set of GGP tournament games with automatically generated rules, targeting the General Game Playing community, but it is incremental as it builds on existing procedural content generation and GDL translation methods.
The paper tackled the problem of automatically generating Simplified Boardgames and translating them into GDL code, using an adaptive evolutionary algorithm with fitness based on simulated playouts to create playable, human-readable, and balanced chess-like games.
We present initial research towards procedural generation of Simplified Boardgames and translating them into an efficient GDL code. This is a step towards establishing Simplified Boardgames as a comparison class for General Game Playing agents. To generate playable, human readable, and balanced chess-like games we use an adaptive evolutionary algorithm with the fitness function based on simulated playouts. In future, we plan to use the proposed method to diversify and extend the set of GGP tournament games by those with fully automatically generated rules.