## 1 Introduction  
In real emergency environments, unprotected evacuees may be seriously injured and even immobilized, due to hazards, or may be trapped in disasters and find that there is no way out [1]. As a result, they need emergency personnel [2] to provide assistance and to take them out of the danger. Rescuers can move to the target locations quickly and protect themselves and evacuees from the danger by wearing special clothes and using protective devices, so that they can make a great contribution toward saving people from hazards [1]. However, the rescuers may run into danger and fail in saving a victim, since they move into hazardous sites, and they may not know which victim has been allocated a rescuer or which rescuer has been assigned a victim if there is no interaction between each assignment. These uncertain factors lead to a waste of resources and the inefficiency of resource allocations. Moreover, during an ongoing hazard, the decision making of how to allocate rescuers to victims requires a fast computational algorithm to provide advice in real-time. Therefore, a fast and efficient assignment approach that considers the possibility of unsuccessful rescue and the influence of other assignments should be addressed. The paper investigates the problem of how to dispatch rescuers to save people out of danger efficiently when a limited set of rescuers needs to be allocated simultaneously to a set of injured civilians [3]. This is a kind of task assignment problem that aims at optimally matching the elements of two or more sets and reducing the overall cost of associated matchings [4]. Here, only two sets will be considered, which we refer to as \"tasks (_i.e._, injured civilians)\" and \"resources (_i.e._, rescuers)\". Previous work [3] proposes that the random neural network (RNN) can be applied to resource allocation problems arising in emergency management to provide a decision quickly and exactly. Thus, we incorporate RNN-based techniques into a practical and realistic model where the evacuees and rescuers are modelled as agents with the help of the Distributed Building Evacuation Simulator (DBES), and the cost for each assignment is considered. Additionally, we test the effectiveness of the RNN-based algorithm for task assignments in an emergency. In the RNN, every possible assignment is considered to be a neuron, and all neurons in the network are fully connected, that is, each executed assignment can have an effect on other allocations through the links between them. Furthermore, there is a cost objective function that can be formulated considering any uncertain parameters (_i.e._, RNN parameters) defined as needed, and an optimal assignment will minimize the expected cost. In this case, we can pick out an assignment that can maximally reduce the overall expected cost by updating RNN parameters iteratively until convergence, and then, this assignment will be executed. The DBES multi-agent environment can capture the position and action of people in the area by using RFIDdevices and audiovisual sensors [5]; thus, if the evacuees are at stake in the building, the sensors will send a message to the rescuers and ask for help. The message received by the rescuers can include detailed information of the injured civilians, such as their ID and position. By applying the RNN-based algorithm, an assignment with the highest active probability will be executed. The duty of rescuers is then to move to the locations of the injured civilians, relying on the information received, and lead or take them to the best evacuation exit or assembly point. The rescuers will wear protective clothes to be less susceptible to injuring themselves, and they will carry protective devices to assist evacuees. The remainder of the paper is organized as follows. Section 2 summarizes the related work on emergency management and the RNN-based techniques. Section 3 describes the design details of the RNN-based task assignment algorithm.