Optimizing Observation Strategy in Emergency Response by Combining Bayesian Network and Multi-Criteria Decision Analysis

Within the realm of protection of infrastructures, it is essential to quickly identify potential risks in case of safety or security incidents. When alarms are triggered, the full extent of threats or damages is sometimes not clear. For example, unknown hazardous materials may be released or the structural integrity of a building could be compromised. In such cases, reconnaissance activities are required. Here, we study how the usage of autonomous systems equipped with portable sensors may support scenario identification and thus might help to decrease risks for emergency response personnel during scenario exploration. The process of reconnaissance can be viewed as an optimisation problem with many different criteria that affect the selection process of an optimal route through a location. Besides the gain in information about the situation, other criteria such as the safety of the autonomous system should be considered. As these criteria can be conflicting, the application of multi-criteria decision analysis (MCDA) methods might proof beneficial. In this work, we present a first approach to optimise the observation strategy in emergency response. A Bayesian network is established to infer key aspects of the situation based on new information provided by the sensors of the autonomous system. A sequential multi-criteria decision analysis is performed based on predefined criteria and current information obtained from the Bayesian network. The approach is illustrated by a simplified generic case study of a small building with multiple rooms. First results show that even simplified situations may lead to complex decision-making processes.

Keywords: Emergency response, Scenario identification, Robotics, Critical infrastructure protection, Bayesian network, Multi-criteria decision analysis, PROMETHEE.

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