Towards a Model-Driven Framework for Simulating Interactive Emergency Response Environments

Abstract

Due to the increasing occurrence of unexpected events and the need for pre-crisis planning to reduce risks and losses, modeling emergency response environments (ERE) is needed more than ever. Modeling may lead to more careful planning for crisis-response operations, such as team formation, task assignment, and doing the task by teams. ERE-ML is a model-driven framework which allows a crisis manager to model an ERE, and to automatically generate the executable code of a multi-agent system (MAS) for that environment. However, the application generated by ERE-ML lacks the capability of supporting interactions among the agents and the organizations involved in the crisis management. In this paper, we propose ERE-ML 2.0 as an upgrade of the previous framework. The ERE-ML 2.0 framework supports the interactions by adding new features to the ERE-ML language, modifying the transformation code, and extending the platform. To evaluate the upgraded framework, the Plasco Tower Collapse incident is modeled, and then the model is transformed into the executable code of a MAS to visualize the run-time scenarios.