Automatic Track Generation for High-End Racing Games Using Evolutionary Computation

摘要

In this paper, we investigate the application of evolutionary computation to the automatic generation of tracks for high-end racing games. The idea underlying our approach is that diversity is a major source of challenge/interest for racing tracks and, eventually, might play a key role in contributing to the player's fun. In particular, we focus on the diversity of a track in terms of its shape (i.e., the number and the assortment of turns and straights it contains), and in terms of driving experience it provides (i.e., the range of speeds achievable while driving on the track). We define two fitness functions that capture our idea of diversity as the entropy of the track's curvature and speed profiles. We apply both a single-objective and a multiobjective real-coded genetic algorithm (GA) to evolve tracks involving both a wide variety of turns and straights and also a large range of driving speeds. The results we report show that both single-objective and multiobjective approaches can successfully evolve tracks with a high degree of diversity both in terms of shape and achievable speeds.