Fatemeh Hendijani Fard, Cooper Davies, & Frank Mauer. (2017). Agile Emergency Responses Using Collaborative Planning HTN. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 857–867). Albi, France: Iscram.
Abstract: Emergency response planning is a complex task due to multiple organizations involved, different planning considerations, etc. Using artificial intelligence collaborative planning helps in the automatic planning for complex situations. Analyzing all impacting factors along with plans that are executable can facilitate the decision making in Emergency Operations Centers for an agile emergency response. A main component of a planner is a knowledge base. Although many systems are developed to support decision making in emergency response or recovery, they either focus on specific or small organizations, or rely on simulations. To the best of our knowledge, there is a gap that there is no common knowledge base for provincial level mass emergencies for automatic planners. The multiplicity of the emergency response documents and their structure makes the knowledge acquisition complex. In this paper, we explain the process of extracting knowledge based on hierarchical task networks and how it speeds up the reactivity to a disaster.
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Tobias Andersson Granberg, Carl-Oscar Jonson, Erik Prytz, Krisjanis Steins, & Martin Waldemarsson. (2020). Sensor Requirements for Logistics Analysis of Emergency Incident Sites. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 952–960). Blacksburg, VA (USA): Virginia Tech.
Abstract: Using sensors to collect data at emergency incident sites can facilitate analysis of the logistic operations. This can be used to improve planning and preparedness for new operations. Furthermore, real-time information from the sensors can serve as operational decision support. In this work in progress, we investigate the requirements on the sensors, and on the sensor data, to facilitate such an analysis. Through observations of exercises, the potential of using sensors for data collection is explored, and the requirements are considered. The results show that the potential benefits are significant, especially for tracking patients, and understanding the interaction between the response actors. However, the sensors need to be quite advanced in order to capture the necessary data.
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