Konstantinos Sfikas

Konstantinos Sfikas, Antonios Liapis, Georgios N. Yannakakis

A core challenge of evolutionary search is the need to balance between exploration of the search space and exploitation of highly fit regions. Quality-diversity search has explicitly walked this tightrope between a population's diversity and its quality. This paper extends a popular quality-diversity search algorithm, MAP-Elites, by treating the selection of parents as a multi-armed bandit problem. Using variations of the upper-confidence bound to select parents from under-explored but potentially rewarding areas of the search space can accelerate the discovery of new regions as well as improve its archive's total quality. The paper tests an indirect measure of quality for parent selection: the survival rate of a parent's offspring. Results show that maintaining a balance between exploration and exploitation leads to the most diverse and high-quality set of solutions in three different testbeds.

Konstantinos Sfikas, Antonios Liapis

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.

Konstantinos Sfikas, Antonios Liapis

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.