Rinascimento: searching the behaviour space of Splendor
  The use of Artificial Intelligence (AI) for play-testing is still on the
sidelines of main applications of AI in games compared to performance-oriented
game-playing. One of the main purposes of play-testing a game is gathering data
on the gameplay, highlighting good and bad features of the design of the game,
providing useful insight to the game designers for improving the design. Using
AI agents has the potential of speeding the process dramatically. The purpose
of this research is to map the behavioural space (BSpace) of a game by using a
general method. Using the MAP-Elites algorithm we search the hyperparameter
space Rinascimento AI agents and map it to the BSpace defined by several
behavioural metrics. This methodology was able to highlight both exemplary and
degenerated behaviours in the original game design of Splendor and two
variations. In particular, the use of event-value functions has generally shown
a remarkable improvement in the coverage of the BSpace compared to agents based
on classic score-based reward signals.
