Coordination in Disaster Management and Response: A Unified Approach

Natural, technological and man-made disasters are typically followed by chaos that results from an inadequate overall response. Three separate levels of coordination are addressed in the mitigation and preparedness phase of disaster management where environmental conditions are slowly changing: (1) communication and transportation infrastructure, (2) monitoring and assessment tools, (3) collaborative tools and services for information sharing. However, the nature of emergencies is to be unpredictable. Toward that end, a fourth level of coordination --- distributed resource/role allocation algorithms of first responders, mobile workers, aid supplies and victims --- addresses the dynamic environmental conditions of the response phase during an emergency. A tiered peer-to-peer system architecture could combine those different levels of coordination to address the changing needs of disaster management. We describe in this paper the architecture of such a tiered peer-to-peer agent-based coordination decision support system for disaster management and response and the applicable coordination algorithms including ATF, a novel, self-organized algorithm for adaptive team formation.

[1]  Milind Tambe,et al.  Team Oriented Programming and Proxy Agents: The Next Generation , 2003, PROMAS.

[2]  Paul Scerri,et al.  Comparing Three Approaches to Large-Scale Coordination , 2006 .

[3]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[4]  Paul Scerri,et al.  Coordinating very large groups of wide area search munitions , 2004 .

[5]  Victor R. Lesser,et al.  Solving distributed constraint optimization problems using cooperative mediation , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[6]  Tiziana Catarci,et al.  WORKPAD: an Adaptive Peer-to-Peer Software Infrastructure for Supporting Collaborative Work of Human Operators in Emergency/Disaster Scenarios , 2006, International Symposium on Collaborative Technologies and Systems (CTS'06).

[7]  François Bourgeois,et al.  An extension of the Munkres algorithm for the assignment problem to rectangular matrices , 1971, CACM.

[8]  William A. Sethares,et al.  Avoiding global congestion using decentralized adaptive agents , 2001, IEEE Trans. Signal Process..

[9]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[10]  Michael J. North,et al.  Experiences creating three implementations of the repast agent modeling toolkit , 2006, TOMC.

[11]  Edmund H. Durfee,et al.  Scaling Up Agent Coordination Strategies , 2001, Computer.

[12]  Clifford Stein,et al.  Introduction to Algorithms, 2nd edition. , 2001 .

[13]  Myriam Abramson Three Myths about Roles , 2005 .

[14]  W. Arthur Inductive Reasoning and Bounded Rationality , 1994 .

[15]  Wendi Heinzelman,et al.  Proceedings of the 33rd Hawaii International Conference on System Sciences- 2000 Energy-Efficient Communication Protocol for Wireless Microsensor Networks , 2022 .

[16]  K. Phillips-Fein The 9/11 Commission Report , 2007 .

[17]  Sarit Kraus,et al.  Adaptive Robotic Communication using Coordination Costs for Improved Trajectory Planning , 2006, AAAI Spring Symposium: Distributed Plan and Schedule Management.

[18]  T. Kean,et al.  The 9/11 Commission Report , 2008 .