This thesis provides an artificial intelligence approach for the problem of resource allocation in time-critical dynamic environments. Motivated by healthcare scenarios such as mass casualty incidents, we are concerned with making effective decisions about allocating to patients the limited resources of ambulances, doctors and other medical staff members, in real-time, under changing circumstances. We cover two distinct stages: the Ambulance stage (at the location of the incident) and the Hospital stage (where the patient requires treatment). Our work addresses both determining the best allocation and supporting decision making (for medical staff to explore possible options). Our approach uses local search with social welfare functions in order to find the best allocations, making use of a centralized tracking of patients and resources. We also clarify how sensing can assist in updating the central system with new information. A key concept in our solution is that of a policy that attempts to minimize cost and maximize utility. To confirm the value of our approach, we present a series of detailed simulations of ambulance and hospital scenarios, and compare algorithms with competing principles of allocation (e.g. sickest first) and societal preferences (e.g. egalitarian allotment). In all, we offer a novel direction for resource allocation that is principled and that offers quantifiable feedback for professionals who are engaged in making resource allocation decisions
