Quality-Diversity in Human Computation

Seth Cooper
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Abstract

Human computation, applying human problem solving to computational problems, has shown promise in numerous applications. In some applications of human computation, it may be useful to find not just a single best solution, but a variety of good solutions with different properties that can be used for further analysis. Recent work in quality-diversity search, such as MAP-Elites, has developed techniques that aim to find a variety of solutions. Thus, in this work, we explore the potential of combining quality-diversity and human computation approaches. We ran a crowdsourced study of the Traveling Salesperson Problem in which some participants were provided with a visualization of their MAP-Elites archive and some were not. We did not find a difference in the quality of the best solution found between the two groups, but did find that participants provided with the archive visu- alization searched more of the MAP-Elites behavior space than those without the visualization. This demonstrates the potential of quality-diversity approaches to impact human computation search.
人类计算中的质量多样性
人类计算,将人类解决问题的方法应用于计算问题,已经在许多应用中显示出前景。在人类计算的某些应用程序中,不仅要找到一个最佳解决方案,还要找到具有不同属性的各种良好解决方案,这些解决方案可用于进一步分析,这可能很有用。最近在质量多样性搜索方面的工作,如MAP-Elites,已经开发出旨在找到各种解决方案的技术。因此,在这项工作中,我们探索了结合质量多样性和人工计算方法的潜力。我们对旅行销售人员问题进行了众包研究,其中一些参与者获得了他们的地图精英档案的可视化,而另一些则没有。我们没有发现两组之间最佳解决方案的质量差异,但确实发现提供档案可视化的参与者比没有可视化的参与者搜索更多的MAP-Elites行为空间。这证明了质量多样性方法影响人类计算搜索的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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