Efficient response to natural disasters has an increasingly important role in limiting the toll on human life and property. The work we have undertaken seeks to improve existing models by building a Decision Support System (DSS) of resource allocation and planning for natural disaster emergencies in urban areas. A multi-agent environment is used to simulate disaster response activities, taking into account geospatial, temporal and rescue organizational information. The problem we address is the acquisition of situated expert knowledge that is used to organize rescue missions. We propose an approach based on participatory design and interactive learning which incrementally elicits experts’ preferences by online analysis of their interventions with rescue simulations. An additive utility functions are used, assuming mutual preferential independence between decision criteria, as a preference for the elicitation process. The learning algorithm proposed refines the coefficients of the utility function by resolving incremental linear programming. For testing our algorithm, we run rescue scenarios of ambulances saving victims. This experiment makes use of geographical data for the Ba-Dinh district of Hanoi and damage parameters from well-regarded local statistical and geographical resources. The preliminary results show that our approach is initially confident in solving this problem.

disaster response, multi-criteria decision making, decision support system, multi-agent simulation, interactive learning, preference elicitation, utility function, participatory design