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RESEARCH PRODUCT

Knowledge-driven multi-agent simulation engineering for assessing the effectiveness of disaster management plans

subject

description

Protecting humans from disasters has been an active mission of governments and experts through the definition of disaster management plans. Defining disaster response strategies is crucial in order to reduce the number of victims and the economic impact. In order to select which response plan is best suited to a specific disaster situation, these plans must be evaluated. However, such evaluation is limited by the high cost of exercises and the specificity of existing simulation models. The approach defended in this thesis combines techniques from Semantic Web and multi-agent simulation to evaluate disaster management response plans. It is composed of four steps : (1) modeling disaster management knowledge, (2) modeling simulations, (3) designing simulations, and (4) analyzing simulation results based on clustering. First, explicit expert knowledge and data is used to create a knowledge model for disaster management. Second, simulation models are conceived based on the knowledge model. Thirdly, generative programming is used for simulation design. Finally, simulation results are used to calculate the plan’s effectiveness for each simulation. Unsupervised learning clustering identifies the application context related to the calculated effectiveness. The effectiveness and the associated application context enrich the initial knowledge model. This approach was applied to a case study based on the French NOVI plan in the city of Montbard, France.