Evolving Evaluation Functions for Collectible Card Game AI
  In this work, we presented a study regarding two important aspects of
evolving feature-based game evaluation functions: the choice of genome
representation and the choice of opponent used to test the model. We compared
three representations. One simpler and more limited, based on a vector of
weights that are used in a linear combination of predefined game features. And
two more complex, based on binary and n-ary trees. On top of this test, we also
investigated the influence of fitness defined as a simulation-based function
that: plays against a fixed weak opponent, plays against a fixed strong
opponent, and plays against the best individual from the previous population.
For a testbed, we have chosen a recently popular domain of digital collectible
card games. We encoded our experiments in a programming game, Legends of Code
and Magic, used in Strategy Card Game AI Competition. However, as the problems
stated are of general nature we are convinced that our observations are
applicable in the other domains as well.
