We study optimal fidelity selection for a human operator servicing a queue of homogeneous tasks. The agent can service a task with a normal or high fidelity level, where fidelity refers to the degree of exactness and precision while servicing the task. Therefore, high-fidelity servicing results in higher-quality service but leads to larger service times and increased operator tiredness. We treat the human cognitive state as a lumped parameter that captures psychological factors such as workload and fatigue. The operator's service time distribution depends on her cognitive dynamics and the fidelity level selected for servicing the task. Her cognitive dynamics evolve as a Markov chain in which the cognitive state increases with high probability whenever she is busy and decreases while resting. The tasks arrive according to a Poisson process and the operator is penalized at a fixed rate for each task waiting in the queue. We address the trade-off between high-quality service of the task and consequent penalty due to a subsequent increase in queue length using a discrete-time Semi-Markov Decision Process framework. We numerically determine an optimal policy and the corresponding optimal value function. Finally, we establish the structural properties of an optimal fidelity policy and provide conditions under which the optimal policy is a threshold-based policy.

翻译：我们研究了同质任务队列中人类操作员的最优保真度选择问题。操作员可以以普通或高保真度级别处理任务，其中保真度指处理任务时的精确度和准确性程度。因此，高保真度服务能提供更高质量的服务，但会导致更长的服务时间和操作员疲劳度的增加。我们将人类认知状态视为一个集总参数，用以捕捉工作负荷与疲劳等心理因素。操作员的服务时间分布取决于其认知动态以及所选的任务处理保真度级别。其认知动态演化为一个马尔可夫链，当操作员处于忙碌状态时，认知状态以高概率增加，而在休息时则降低。任务按照泊松过程到达，每个在队列中等待的任务将以固定速率对操作员施加惩罚。我们采用离散时间半马尔可夫决策过程框架，权衡任务的高质量服务与因队列长度增加而产生的后续惩罚。通过数值方法确定了最优策略及对应的最优值函数。最后，我们建立了最优保真度策略的结构特性，并给出了最优策略为基于阈值策略的条件。