Does it matter how well I know what you're thinking? Opponent Modelling
  in an RTS game
  Opponent Modelling tries to predict the future actions of opponents, and is
required to perform well in multi-player games. There is a deep literature on
learning an opponent model, but much less on how accurate such models must be
to be useful. We investigate the sensitivity of Monte Carlo Tree Search (MCTS)
and a Rolling Horizon Evolutionary Algorithm (RHEA) to the accuracy of their
modelling of the opponent in a simple Real-Time Strategy game. We find that in
this domain RHEA is much more sensitive to the accuracy of an opponent model
than MCTS. MCTS generally does better even with an inaccurate model, while this
will degrade RHEA's performance. We show that faced with an unknown opponent
and a low computational budget it is better not to use any explicit model with
RHEA, and to model the opponent's actions within the tree as part of the MCTS
algorithm.
