Map Generation and Balance in the Terra Mystica Board Game Using Particle Swarm and Local Search

Abstract

Modern board games offer an interesting opportunity for automatically generating content and models for ensuring balance among players. This paper tackles the problem of generating balanced maps for a popular and sophisticated board game called Terra Mystica. The complexity of the involved requirements coupled with a large search space makes of this a complex combinatorial optimisation problem which has not been investigated in the literature, to the best of the authors’ knowledge. This paper investigates the use of particle swarm optimisation and steepest ascent hill climbing with a random restart for generating maps in accordance with a designed subset of requirements. The results of applying these methods are very encouraging, fully showcasing the potential of search-based metaheuristics in procedural content generation.