  While artificial intelligence has been applied to control players' decisions
in board games for over half a century, little attention is given to games with
no player competition. Pandemic is an exemplar collaborative board game where
all players coordinate to overcome challenges posed by events occurring during
the game's progression. This paper proposes an artificial agent which controls
all players' actions and balances chances of winning versus risk of losing in
this highly stochastic environment. The agent applies a Rolling Horizon
Evolutionary Algorithm on an abstraction of the game-state that lowers the
branching factor and simulates the game's stochasticity. Results show that the
proposed algorithm can find winning strategies more consistently in different
games of varying difficulty. The impact of a number of state evaluation metrics
is explored, balancing between optimistic strategies that favor winning and
pessimistic strategies that guard against losing.
