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Tina Comes, Claudine Conrado, Michael Hiete, Michiel Kamermans, Gregor Pavlin, & Niek Wijngaards. (2010). An intelligent decision support system for decision making under uncertainty in distributed reasoning frameworks. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper presents an intelligent system facilitating better-informed decision making under severe uncertainty as found in emergency management. The construction of decision-relevant scenarios, being coherent and plausible descriptions of a situation and its future development, is used as a rationale for collecting, organizing, filtering and processing information for decision making. The development of scenarios is geared to assessing decision alternatives, thus avoiding time-consuming analysis and processing of irrelevant information. The scenarios are constructed in a distributed setting allowing for a flexible adaptation of reasoning (principles and processes) to the problem at hand and the information available. This approach ensures that each decision can be founded on a coherent set of scenarios, which was constructed using the best expertise available within a limited timeframe. Our theoretical framework is demonstrated in a distributed decision support system by orchestrating both automated systems and human experts into workflows tailored to each specific problem.
Keywords: Automation; Civil defense; Decision making; Decision support systems; Disasters; Expert systems; Information systems; Intelligent systems; Multi agent systems; Risk management; Decision making under uncertainty; Distributed decision support systems; Distributed reasonings; Emergency management; Intelligent decision support systems; Multi-criteria decision analysis; Scenario-based; Theoretical framework; Information filtering
Track: Intelligent Systems
Ali Khalili-Araghi, Uwe Glässer, Hamed Yaghoubi Shahir, Brian Fisher, & Piper Jackson. (2012). Intelligent decision support for emergency responses. 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: With a coastline touching upon the Pacific and Atlantic Oceans, the Great Lakes and the Arctic Sea, the Canadian MSOCs are faced with a daunting task. They are responsible for both routine duties, including patrolling coastal areas and collecting satellite data, as well as critical missions, such as emergency response and crime intervention. Both kinds of mission require the fusion of data from a variety of sources and the orchestration of myriad heterogeneous resources over great physical distances. They must deal with uncertainty, both in terms of what can be known and also in the outcomes of actions, and must interact with an environment prone to dynamic change. We present the architecture and core mechanisms of a decision support system for marine safety and security operations (GlÃ¤sser, Jackson, Araghi, When and Shahir, 2010). The goal of this system is to enhance complex command and control tasks by improving situational awareness and automating task assignments. This system concept includes adaptive information fusion techniques integrated with decentralized control mechanisms for dynamic resource configuration management and task execution management under uncertainty. Autonomously operating agents employ collaboration and coordination to collectively form an intelligent decision support system. Â© 2012 ISCRAM.
Keywords: Artificial intelligence; Command and control systems; Decision support systems; Dynamics; Information systems; Intelligent agents; Adaptive information; Command and control; Dynamic resource configurations; Emergency response; Heterogeneous resources; Intelligent decision support; Intelligent decision support systems; Situational awareness; Information management
Track: Poster Session