通过质量多样性生成程序内容
Procedural Content Generation through Quality Diversity
摘要：
质量多样性（QD）算法搜索一组好的解决方案，覆盖的空间定义的行为度量。这种同时关注质量和多样性与明确的指标集QD算法除了标准的单目标和多目标进化算法，以及从多样性保护方法，如小生境。这些属性为游戏中的人工智能开辟了新的途径，特别是对于程序内容生成。创建多个系统性变化的解决方案可以为创造性的人机交互和适应性提供新的方法。在过去的几年中，一些应用程序的QD程序的内容生成和玩游戏已经提出，我们讨论这些问题，并提出未来工作的挑战。
英文摘要：
Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics. This simultaneous focus on quality and diversity with explicit metrics sets QD algorithms apart from standard single- and multi-objective evolutionary algorithms, as well as from diversity preservation approaches such as niching. These properties open up new avenues for artificial intelligence in games, in particular for procedural content generation. Creating multiple systematically varying solutions allows new approaches to creative human-AI interaction as well as adaptivity. In the last few years, a handful of applications of QD to procedural content generation and game playing have been proposed; we discuss these and propose challenges for future work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
