Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic

Competitive board games have provided a rich and diverse testbed for artificial intelligence. This article 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 article 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, macroaction 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 multiplayer collaboration interactions.