Marinus Maris, & Gregor Pavlin. (2006). Distributed perception networks for crisis management. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 376–381). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Situation assessment in crisis management applications can be supported by automated information fusion systems, such as Distributed Perception Networks. DPNs are self-organizing fusion systems that can infer hidden events through interpretation of huge amounts of heterogeneous and noisy observations. DPNs are a logical layer on top of existing communication, sensing, processing and data storage infrastructure. They can reliably and efficiently process information of various quality obtained from humans and sensors through the existing communication systems, such as mobile phone networks or internet. In addition, modularity of DPNs supports efficient design and maintenance of very complex fusion systems. In this paper, a fully functional prototype of a DPN system is presented that fuses information from gas sensors and human observations. The task of the system is to compute probability values for the hypothesis that a particular gas is present in the environment. It is discussed how such a system could be used for crisis management.
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Gabriel Jakobson, Nandan Parameswaran, John Buford, Lundy Lewis, & Pradeep Ray. (2006). Situation-Aware multi-Agent system for disaster relief operations management. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 313–324). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Natural and human-made disasters create unparalleled challenges to Disaster Situation Management (DSM). One of the major weaknesses of the current DSM solutions is the lack of comprehensive understanding of the overall disaster operational situation, and very often making decisions based on a single event. Such weakness is clearly exhibited by the solutions based on the widely used Belief-Desire-Intention (BDI) models for building the Muiti-Agent Systems (MAS). In this work we present the adaptation of the AESOP situation management architecture to address the requirements of disaster relief operations. In particular, we extend the existing BDI model with the capability of situation awareness. We describe how the key functions of event collection, situation identification, and situation assessment are implemented in MAS architecture suitable to the characteristics of large-scale disaster recovery. We present the details of a BDI agent in this architecture including a skeleton ontology, and the distributed service architecture of the AESOP platform.
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John M. McGuirl, Nadine B. Sarter, & David D. Woods. (2008). Seeing is believing?: The effects of real-time, image-based feedback on emergency management decision-making. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 406–414). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Emergency management personnel often face feedback delays and a lack of reliable information. To address this problem, new information technologies have been developed that can provide real-time, image-based feedback. While potentially useful, this trend represents a fundamental shift in both the timing and format of the information used by incident commanders (ICs). Eight ICs took part in a simulation exercise to determine the potential impact of real-time imaging on their decision-making. Nearly all of the ICs failed to detect important changes in the situation that were not captured in the imaging but that were available via other, more traditional data sources. It appears that the ICs placed an inappropriately high level of trust in the imaging data, resulting in reduced data search activities and hypothesis generation. This research helps practitioners anticipate and guard against undesirable effects of introducing similar technologies on training and operational procedures.
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Yixing Shan, Lili Yang, & Roy Kalawsky. (2014). Exploring the prescriptive modeling of fire situation assessment. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 60–64). University Park, PA: The Pennsylvania State University.
Abstract: One of the key assumptions in Endsley's three-level Situation Awareness (SA) model is the critical role of mental models in the development and maintenance of SA. We explored a prescriptive way of modeling this essential mental process of the fire incident commanders' fire ground assessment. The modeling was drawn from the Fast and Frugal Heuristics (FFHs) program, given the strong parallels between its contentions on ecological rationality and the environment demanding of the emergency response context. This paper addresses a number of issues being encountered in the attempt of our empirical investigation.
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Rita Kovordanyi, Rudolf Schreiner, Jelle Pelfrene, Johan Jenvald, Henrik Eriksson, Amy Rankin, et al. (2012). Real-time support for exercise managers' situation assessment and decision making. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Exercise managers and instructors have a particularly challenging task in monitoring and controlling on-going exercises, which may involve multiple response teams and organizations in highly complex and continuously evolving crisis situations. Managers and instructors must handle potentially incomplete and conflicting field-observation data and make decisions in real-time in order to control the flow of the exercise and to keep it in line with the training objectives. In simulation-based exercises, managers and instructors have access to a rich set of real-time data, with an increased potential to closely monitor the trainees' actions, and to keep the exercise on track. To assist exercise managers and instructors, data about the on-going exercise can be filtered, aggregated and refined by real-time decision-support systems. We have developed a model and a prototype decision-support system, using stream-based reasoning to assist exercise managers and instructors in real-time. The approach takes advantage of topic maps for ontological representation and a complex-event processing engine for analyzing the data stream from a virtual-reality simulator for crisis-management training. Aggregated data is presented both on-screen, in Twitter, and in the form of topic maps. © 2012 ISCRAM.
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