中文摘要：

在工作流管理系统中，任务分派策略对工作流系统的性能影响较大，而人力资源社会属性的不稳定也给任务分派带来了挑战．一般的任务分派篡略还存在以下问题：分派时只考虑候选资源的个体属性，忽略了流程中其他资源对候选资源的影响；需要为候选资源预先设置能力指标，但预设指标很难与候选资源的实际情况吻合，错误的能力指标会导致将任务分派给不合适的资源，降低工作流系统的性能．为克服上述问题，基于不同的状态转移视角和奖励函数，提出了4种基于Q学习的任务分派算法通过对比实验，论证了基于Q学习的任务分派算法在未预设资源能力的情况下仍能取得较好效果，且支持在任务分派过程中考虑社会关系的影响，使得平均案例完成时间进一步降低．

英文摘要：

Task assignment strategy has a great impact on the performance of the workflow management system. The instability of human resource brings challenges to task assignment. General task assignment strategies have some deficiencies. First, they only consider the individual attributes of candidate resources, ignoring the influences to the candidate resources from other resources in process. In addition, they need to setup a capability index of each resource in advance. However, it is hard to make the capability index fit the actual situation, and a wrong capability index will make the workflow engine assign the task to the unsuitable resource, degrading the performance of workflow management system. To overcome the above deficiencies, four Q-learning-based task assignment algorithms are proposed according to different state transition views and different reward functions. Simulation experiments show that Q-learning-based task assignment algorithms can work well even without setting up a capability index in advance. Also due to their support to consider the social relationship, the average time of case completion decreases.