论文标题
替代性健身获取FI-2POP用于程序内容生成
Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation
论文作者
论文摘要
当使用程序内容生成（PCG）为视频游戏生成内容时，目标是创建高质量的功能性资产。先前的工作通常利用了可行的两种构造（FI-2POP）对PCG的约束优化算法，有时与表型 - 精英（MAP-Elites）算法的多维档案结合使用，以找到一组多元化的解决方案。但是，不可行人群的适应性功能仅考虑了违反的约束数量。在本文中，我们介绍了FI-2POP的一种变体，其中培训了一个替代模型，以预测可行的父母可行儿童的适应性，这是由于产生可行儿童的可能性加权。这推动了朝着更高的，可行的解决方案进行选择。我们展示了我们的方法，即为太空工程师生成太空飞船的任务，对标准FI-2POP以及最新的多发射极限制的MAP-ELITE算法的改进。
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.
