  Competitive board games have provided a rich and diverse testbed for
artificial intelligence. This paper contends that collaborative board games
pose a different challenge to artificial intelligence as it must balance
short-term risk mitigation with long-term winning strategies. Collaborative
board games task all players to coordinate their different powers or pool their
resources to overcome an escalating challenge posed by the board and a
stochastic ruleset. This paper focuses on the exemplary collaborative board
game Pandemic and presents a rolling horizon evolutionary algorithm designed
specifically for this game. The complex way in which the Pandemic game state
changes in a stochastic but predictable way required a number of specially
designed forward models, macro-action representations for decision-making, and
repair functions for the genetic operations of the evolutionary algorithm.
Variants of the algorithm which explore optimistic versus pessimistic game
state evaluations, different mutation rates and event horizons are compared
against a baseline hierarchical policy agent. Results show that an evolutionary
approach via short-horizon rollouts can better account for the future dangers
that the board may introduce, and guard against them. Results highlight the
types of challenges that collaborative board games pose to artificial
intelligence, especially for handling multi-player collaboration interactions.
