The classification of the degree of certainty.

Automated negotiation agents require human-like adaptability in emotionally charged and time-constrained settings. This study introduces an Emotion-Time Dual-Process Framework that integrates the Appraisal Tendency Framework with dynamic temporal modeling. Emotions are decomposed into pleasantness and certainty dimensions and mapped to six emotional persuasion strategies. A variable-rate time function is designed to capture the perceptions of dynamic time pressure. Emotion and time pressure jointly drive a state-dependent concession updating model. The proposed framework was validated through a series of simulation experiments based on different scenarios. The results demonstrate that the proposed framework has significant advantages in improving negotiation success rates, joint utility, and outcome fairness against baseline models. In particular, incorporating emotional factors reduces utility disparity between parties by 28.55%, while the proposed time function improves negotiation efficiency by 12.99% without sacrificing fairness or the success rate. This study provides a theorical basis for developing highly more human-like and adaptive intelligent negotiation systems.

自动化协商智能体（Automated negotiation agents）需要在带有情绪色彩且受时间约束的场景中具备类人的适应性。本研究提出了一种情绪-时间双过程框架（Emotion-Time Dual-Process Framework），将评估倾向框架（Appraisal Tendency Framework）与动态时间建模相结合。情绪被分解为愉悦度与确定性两个维度，并映射至六种情绪说服策略。研究设计了可变速率时间函数以捕捉动态时间压力的感知。情绪与时间压力共同驱动了一个状态依赖型让步更新模型。所提出的框架通过一系列基于不同场景的仿真实验得到了验证。实验结果表明，相较于基准模型，所提框架在提升协商成功率、联合效用以及结果公平性方面具备显著优势。具体而言，纳入情绪因素可使双方的效用差距降低28.55%，而所提出的时间函数可在不牺牲公平性与成功率的前提下，将协商效率提升12.99%。本研究为开发更具类人性与适应性的智能协商系统提供了理论基础。