Automatic story generation systems usually deliver suspense by including an adverse outcome in the narrative, in the assumption that the adversity will trigger a certain set of emotions that can be categorized as suspenseful. However, existing systems do not implement solutions relying on predictive models of the impact of the outcome on readers. A formulation of the emotional effects of the outcome would allow storytelling systems to perform a better measure of suspense and discriminate among potential outcomes based on the emotional impact. This paper reports on a computational model of the effect of different outcomes on the perceived suspense. A preliminary analysis to identify and evaluate the affective responses to a set of outcomes commonly used in suspense was carried out. Then, a study was run to quantify and compare suspense and affective responses evoked by the set of outcomes. Next, a predictive model relying on the analyzed data was computed, and an evolutionary algorithm for automatically choosing the best outcome was implemented. The system was tested against human subjects’ reported suspense and electromyography responses to the addition of the generated outcomes to narrative passages. The results show a high correlation between the predicted impact of the computed outcome and the reported suspense.

自动故事生成系统通常通过在叙事中包含不利结果来传递悬念，前提是假设逆境会触发某些可以归类为悬念的情绪。但是，现有系统无法实现依赖结果对读者影响的预测模型的解决方案。对结果的情感影响的表述将使叙事系统能够更好地衡量悬念，并根据情感影响来区分潜在的结果。本文报告了一个计算模型，该模型计算了不同结果对感知的悬念的影响。进行了初步分析，以识别和评估对悬念中常用的一组结果的情感反应。然后，进行了一项研究，以量化和比较一组结果引起的悬念和情感反应。接下来，计算了一个依赖于分析数据的预测模型，并实现了一种自动选择最佳结果的进化算法。该系统针对人类受试者报告的悬念和肌电图反应进行了测试，以将产生的结果添加到叙述段落中。结果表明，计算结果的预期影响与所报告的悬念之间具有高度相关性。该系统针对人类受试者报告的悬念和肌电图反应进行了测试，以将产生的结果添加到叙述段落中。结果表明，计算结果的预期影响与所报告的悬念之间具有高度相关性。该系统针对人类受试者报告的悬念和肌电图反应进行了测试，以将产生的结果添加到叙述段落中。结果表明，计算结果的预期影响与所报告的悬念之间具有高度相关性。