Evidence theory is widely used in decision-making and reasoning systems. In previous research, Transferable Belief Model (TBM) is a commonly used evidential decision making model, but TBM is a non-preference model. In order to better fit the decision making goals, the Evidence Pattern Reasoning Model (EPRM) is proposed. By defining pattern operators and decision making operators, corresponding preferences can be set for different tasks. Random Permutation Set (RPS) expands order information for evidence theory. It is hard for RPS to characterize the complex relationship between samples such as cycling, paralleling relationships. Therefore, Random Graph Set (RGS) were proposed to model complex relationships and represent more event types. In order to illustrate the significance of RGS and EPRM, an experiment of aircraft velocity ranking was designed and 10,000 cases were simulated. The implementation of EPRM called Conflict Resolution Decision optimized 18.17\% of the cases compared to Mean Velocity Decision, effectively improving the aircraft velocity ranking. EPRM provides a unified solution for evidence-based decision making.

翻译：证据理论广泛应用于决策与推理系统。在以往研究中，可传递信念模型（TBM）是常用的证据决策模型，但TBM属于无偏好模型。为更好地契合决策目标，提出了证据模式推理模型（EPRM）。通过定义模式算子与决策算子，可为不同任务设定相应偏好。随机置换集（RPS）为证据理论扩展了序次信息，但难以表征样本间循环、并行等复杂关系。为此，提出随机图集（RGS）以建模复杂关系并表征更多事件类型。为阐明RGS与EPRM的重要性，设计了飞行器速度排序实验并模拟了10000个案例。相较于平均速度决策，采用冲突消解决策实现的EPRM对18.17%的案例进行了优化，有效提升了飞行器速度排序效果。EPRM为基于证据的决策提供了统一解决方案。