Suitable rescue path selection is very important to rescue lives and reduce the loss of disasters, and has been a key issue in the field of disaster response management. In this paper, we present a path selection algo...Suitable rescue path selection is very important to rescue lives and reduce the loss of disasters, and has been a key issue in the field of disaster response management. In this paper, we present a path selection algorithm based on Q-learning for disaster response applications. We assume that a rescue team is an agent, which is operating in a dynamic and dangerous environment and needs to find a safe and short path in the least time. We first propose a path selection model for disaster response management, and deduce that path selection based on our model is a Markov decision process. Then, we introduce Q-learning and design strategies for action selection and to avoid cyclic path. Finally, experimental results show that our algorithm can find a safe and short path in the dynamic and dangerous environment, which can provide a specific and significant reference for practical management in disaster response applications.展开更多
Multiorganizational response to emergencies and disasters requires collaboration.How to improve the collective response is therefore an essential question,but not easy to answer.In disaster research,normative research...Multiorganizational response to emergencies and disasters requires collaboration.How to improve the collective response is therefore an essential question,but not easy to answer.In disaster research,normative research with a focus on providing evidence for how to improve professional practice has traditionally received less attention than explanatory ones.The aim of this article,using insights from design science where normative research is more common,is to suggest a complementary approach to response management research.Our approach,which combines experimental and explanatory research,is applied to a study of goal alignment.Goal alignment among response actors is often recommended despite literature’s contradictory evidence regarding its effect.We conducted an experiment with 111 participants,who,in groups of three,played a computer game under one of two conditions(goal alignment or not).Our results show that aligning goals did not improve the outcome in the game.Although this may serve as a counterargument to implementing goal alignment interventions,there are concerns with such conclusions.These reservations include,but are not limited to,the lack of validated models to use in experiments.Nevertheless,our suggested research approach and the goal alignment experiment highlight the importance of testing interventions and their effectiveness before implementation.展开更多
基金supported by National Basic Research Program of China (973 Program) (No. 2009CB326203)National Natural Science Foundation of China (No. 61004103)+5 种基金the National Research Foundation for the Doctoral Program of Higher Education of China (No. 20100111110005)China Postdoctoral Science Foundation (No. 20090460742)National Engineering Research Center of Special Display Technology (No. 2008HGXJ0350)Natural Science Foundation of Anhui Province (No. 090412058, No. 070412035)Natural Science Foundation of Anhui Province of China (No. 11040606Q44, No. 090412058)Specialized Research Fund for Doctoral Scholars of Hefei University of Technology (No. GDBJ2009-003, No. GDBJ2009-067)
文摘Suitable rescue path selection is very important to rescue lives and reduce the loss of disasters, and has been a key issue in the field of disaster response management. In this paper, we present a path selection algorithm based on Q-learning for disaster response applications. We assume that a rescue team is an agent, which is operating in a dynamic and dangerous environment and needs to find a safe and short path in the least time. We first propose a path selection model for disaster response management, and deduce that path selection based on our model is a Markov decision process. Then, we introduce Q-learning and design strategies for action selection and to avoid cyclic path. Finally, experimental results show that our algorithm can find a safe and short path in the dynamic and dangerous environment, which can provide a specific and significant reference for practical management in disaster response applications.
基金The research for this article was financially supported by the Swedish Civil Contingencies Agency.
文摘Multiorganizational response to emergencies and disasters requires collaboration.How to improve the collective response is therefore an essential question,but not easy to answer.In disaster research,normative research with a focus on providing evidence for how to improve professional practice has traditionally received less attention than explanatory ones.The aim of this article,using insights from design science where normative research is more common,is to suggest a complementary approach to response management research.Our approach,which combines experimental and explanatory research,is applied to a study of goal alignment.Goal alignment among response actors is often recommended despite literature’s contradictory evidence regarding its effect.We conducted an experiment with 111 participants,who,in groups of three,played a computer game under one of two conditions(goal alignment or not).Our results show that aligning goals did not improve the outcome in the game.Although this may serve as a counterargument to implementing goal alignment interventions,there are concerns with such conclusions.These reservations include,but are not limited to,the lack of validated models to use in experiments.Nevertheless,our suggested research approach and the goal alignment experiment highlight the importance of testing interventions and their effectiveness before implementation.
