Exploring open-ended gameplay features with Micro RollerCoaster Tycoon
This work addresses incremental questions about evolutionary algorithms and game design for AI researchers and game developers.
The paper tackled the problem of generating diverse and profitable amusement park layouts in the MicroRCT simulator using the MAP-Elites algorithm, finding that starting from scratch with no costs produced the widest diversity of high-performing designs.
This paper introduces MicroRCT, a novel open source simulator inspired by the theme park sandbox game RollerCoaster Tycoon. The goal in MicroRCT is to place rides and shops in an amusement park to maximize profit earned from park guests. Thus, the challenges for game AI include both selecting high-earning attractions and placing them in locations that are convenient to guests. In this paper, the MAP-Elites algorithm is used to generate a diversity of park layouts, exploring two theoretical questions about evolutionary algorithms and game design: 1) Is there a benefit to starting from a minimal starting point for evolution and complexifying incrementally? and 2) What are the effects of resource limitations on creativity and optimization? Results indicate that building from scratch with no costs results in the widest diversity of high-performing designs.