Adaptive multi-agent relief assessment and emergency response

The prediction of relief demand during disasters is an important operational task that should be performed before emergency response is initiated because such prediction enables the appropriate allocation and distribution of humanitarian supplies to affected zones. Current prediction methods appear to over or underestimate the calculation of relief demand-a problem that can be resolved by dispatching assessment vehicles to the affected zones of a region at the onset of a disaster. This study is aimed at facilitating the coordination of relief assessment and emergency response through the development of an adaptive multi-agent demand evaluation and demand-responsive model. Its main objective is to provide a model from which emergency response teams (ERTs) can obtain accurate information that will be used as basis by relief assessment teams (RATs) in effectively distributing humanitarian aid and conducting search and rescue operations. We propose a Markov decision process as a multi-agent assessment and response system, with reinforcement learning designed to ensure the integration of ERT and RAT operations. We use a coordination cluster system to coordinate ERT actors and use the proposed model to solve the problems occurring in a real-size network. Results show that the use of the model can improve emergency response operations and decrease death tolls.