Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation

Code

• github.com/arayabrain/space-engineers-ai-spaceship-generator
  Officialpytorch★ 24

Abstract

When generating content for video games using procedural content generation (PCG), the goal is to create functional assets of high quality. Prior work has commonly leveraged the feasible-infeasible two-population (FI-2Pop) constrained optimisation algorithm for PCG, sometimes in combination with the multi-dimensional archive of phenotypic-elites (MAP-Elites) algorithm for finding a set of diverse solutions. However, the fitness function for the infeasible population only takes into account the number of constraints violated. In this paper we present a variant of FI-2Pop in which a surrogate model is trained to predict the fitness of feasible children from infeasible parents, weighted by the probability of producing feasible children. This drives selection towards higher-fitness, feasible solutions. We demonstrate our method on the task of generating spaceships for Space Engineers, showing improvements over both standard FI-2Pop, and the more recent multi-emitter constrained MAP-Elites algorithm.

Reproductions