Addressing Preemption Costs in Multi-Agent Resource Allocation for Medical Applications

In this paper we offer an approach for reasoning about resource allocation and scheduling in multiagent systems that takes into consideration the costs of preempting an agent from its current task. We apply our methodology to the motivating medical application of allocating doctors to patients in hospitals during mass casualty incidents and demonstrate noticeable improvements in performance (generating far fewer problem patients) over competing approaches that do not model the costs of preemption in sufficient detail. In particular, our approach offers a method for addressing the challenges of cyclical dependencies in the estimation of preemption costs by localized agents through a combination of planning techniques.