Generating Diverse and Competitive Play-Styles for Strategy Games
  Designing agents that are able to achieve different play-styles while
maintaining a competitive level of play is a difficult task, especially for
games for which the research community has not found super-human performance
yet, like strategy games. These require the AI to deal with large action
spaces, long-term planning and partial observability, among other well-known
factors that make decision-making a hard problem. On top of this, achieving
distinct play-styles using a general algorithm without reducing playing
strength is not trivial. In this paper, we propose Portfolio Monte Carlo Tree
Search with Progressive Unpruning for playing a turn-based strategy game
(Tribes) and show how it can be parameterized so a quality-diversity algorithm
(MAP-Elites) is used to achieve different play-styles while keeping a
competitive level of play. Our results show that this algorithm is capable of
achieving these goals even for an extensive collection of game levels beyond
those used for training.
