Towards generating arcade game rules with VGDL

We describe an attempt to generate complete arcade
games using the Video Game Description Language (VGDL)
and the General Video Game Playing environment (GVG-AI).
Games are generated by an evolutionary algorithm working on
genotypes represented as VGDL descriptions. In order to direct
evolution towards good games, we need an evaluation function
that accurately estimates game quality. The evaluation function
used here is based on the differential performance of several
game-playing algorithms, or Relative Algorithm Performance
Profiles (RAPP): it is assumed that good games allow good
players to play better than bad players. For the purpose of such
evaluations, we introduce two new game tree search algorithms,
DeepSearch and Explorer; these perform very well on benchmark
games and constitute a substantial subsidiary contribution of the
paper. In the end, the attempt to generate arcade games is only
partially successful, as some of the games have interesting design
features but are barely playable as generated. An analysis of these
shortcomings yields several suggestions to guide future attempts
at arcade game generation