from: http://codebad.com/
An article by Julian Togelius was recently brought to my attention in
which he discusses his experiments with a game generation evolutionary
algorithm that evaluates game fitness as a function of automated player
training.
I think it's a great idea, if not a terribly obvious one. Julian's
purpose in his quest is a noble one:
Our solution is to use learnability as a predictor of fun. A good
game is one that is not winnable by a novice player, but which the
player can learn to play better and better over time, and eventually
win; it has a smooth learning curve.
I think if you ignore silly animalistic compulsions like collecting
items, finding all the secrets, or acting out fantasies, as well as more
legitimate human animal patterns like appreciating beauty in game audio
or visuals, or social interaction through gaming (hopefully more like
Super Smash Bros than World of Warcraft,) I believe this is an
absolutely solid function for measuring game funness.
It's worth noting here that Togelius does not explicitly state that his
technique is immediately applicable to video games, only heavily
implied. (It should also be noted that by video games I am going to
bravely exclude all turn-based games, relegating further discussion to
games which challenge one to think fast and exercise their reflexes.)
more: http://codebad.com/
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