循环中的专家知识对探索机器合理性的影响
The Impact of Expertise in the Loop for Exploring Machine Rationality
摘要：
人在回路优化利用人类的专业知识来迭代地引导机器优化器并在解空间中搜索最优解。虽然以往的实证研究主要是调查新手，我们分析了专业知识水平对结果质量和相应的主观满意度的影响。我们在文本、照片和3D网格优化环境中进行了一项研究（N=60）。我们发现，新手可以实现专家级的质量性能，但具有更高专业知识的参与者导致更多的优化迭代，更明确的偏好，同时保持满意度低。相比之下，新手更容易满足，也更快结束。因此，我们发现，专家寻求更多样化的结果，而机器达到最佳结果，观察到的行为可以作为性能指标，供人在环系统设计人员改进底层模型。我们告知未来的研究在设计人在环系统时要谨慎考虑用户专业知识的影响。
英文摘要：
Human-in-the-loop optimization utilizes human expertise to guide machine optimizers iteratively and search for an optimal solution in a solution space. While prior empirical studies mainly investigated novices, we analyzed the impact of the levels of expertise on the outcome quality and corresponding subjective satisfaction. We conducted a study (N=60) in text, photo, and 3D mesh optimization contexts. We found that novices can achieve an expert level of quality performance, but participants with higher expertise led to more optimization iteration with more explicit preference while keeping satisfaction low. In contrast, novices were more easily satisfied and terminated faster. Therefore, we identified that experts seek more diverse outcomes while the machine reaches optimal results, and the observed behavior can be used as a performance indicator for human-in-the-loop system designers to improve underlying models. We inform future research to be cautious about the impact of user expertise when designing human-in-the-loop systems.
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