Study on Emergency Decision-Making of Natural Disaster Based on Collaboration of Multi-Agent

2013 ◽  
Vol 411-414 ◽  
pp. 2684-2693
Author(s):  
Yue E ◽  
Ye Ping Zhu
Keyword(s):  
Decision Making ◽  
Natural Disaster ◽  
Decision Making Process ◽  
Collaborative Decision Making ◽  
Agent Theory ◽  
Emergency Decision Making ◽  
Multi Agent ◽  
Emergency Decision ◽  
Decision Making Model ◽  
Collaborative Decision

Based on the occurrence and evolution of the natural disaster is characteristic of uncertainty and complexity, in this article, Agent theory and technology is applied to emergency decision-making of natural disaster in China, built the disaster emergency collaborative decision-making framework based on multi-agent, design the collaborative decision-making model, discuss the emergency decision-making mechanism based on scenario-response, through effective collaboration based on multi-agent, achieve natural disaster dynamic emergency decision-making process.

scholarly journals Cooperative Decision Support Systems

2011 ◽  
pp. 109-115 ◽  
Author(s):  
Pascale Zaraté
Keyword(s):  
Decision Making ◽  
Search Space ◽  
Optimal Decision ◽  
Decision Making Process ◽  
Collaborative Decision Making ◽  
Distributed Reasoning ◽  
Cooperative Decision Making ◽  
Optimal Decision Making ◽  
Analytical Tools ◽  
Collaborative Decision

The subject of our research aims to support in the most suitable way the collaborative decision-making process. Several scientific approaches deal with collaborative decision-making: decision analysis (Carlsson & Turban, 2002; Doyle & Thomason, 1999; Keeney & Raiffa, 1976) developing different analytical tools for optimal decision-making; in management sciences the observation of decision-making styles activity (Nuut, 2005; Fong, Wyer, & Robert 2003); decision-making as a group work (Esser, 1998; Matta & Corby, 1997); studies concerning different types of decisions focalised on number of actors: individual (Keeney & Raiffa, 1976), group (Shim, Warkentin, Courtney, Power, Sharda, & Carlsson, 2002), cooperative (Zaraté, 2005), and collaborative (Karacapilidis & Papadias, 2001). For the collaborative decision-making field, the situation is clear. In most of research studies, the concept of collaborative decision-making is used as a synonym for cooperative decision-making. Hence, the collaborative decision-making process is considered to be distributed and asynchronous (Chim, Anumba, & Carillo, 2004; Cil, Alpturk, & Yazgan, 2005). However, we can stand out several works, having different research approaches, considering collaborative decision-making process as multi-actor decision-making process, where actors have different goals. Considering (Panzarasa, Jennings, & Norman, 2002) the collaborative decision-making process is seen as “a group of logically decentralised agents that cooperate to achieve objectives that are typically beyond the capacities of an individual agent. In short, the collaborative decision-making has generally been viewed and modelled as a kind of distributed reasoning and search, whereby a collection of agents collaboratively go throughout the search space of the problem in order to find a solution.” The main interrogation of this article is to study the best way to support collaborative decision-making process.

Exploring Agent-Based Modeling for Emergency Logistics Collaborative Decision Making

2013 ◽  
Vol 710 ◽  
pp. 781-785 ◽  
Author(s):  
Li Zhang ◽  
Zhi Qi ◽  
Hao Cui ◽  
Sen Hua Wang ◽  
Ya Hui Ning ◽  
...  
Keyword(s):  
Decision Making ◽  
Local Level ◽  
Collaborative Decision Making ◽  
Agent Based ◽  
Concept Model ◽  
Emergency Logistics ◽  
Bdi Agent ◽  
Distribution Process ◽  
Multi Agent ◽  
Collaborative Decision

Aiming at the requirements of urgency and dynamics in emergency logistics, this paper presents a multi-agent system (MAS) concept model for emergency logistics collaborative decision making. The suggested model includes three kinds of agents, i.e., role agent, function agent and assistant agent. Role agent excutes emergency logistics activities, function agent achieves the task requirements in every work phase and assistant agent helps organizing and visiting data. Two levels agent views serve as the basic skeleton of the MAS. Top level is the global decision-making view, which describes the task distribution process with multiple agents. Local level is the execution planning view, which simulates task executing process of the performer. Finally, an extended BDI agent structure model is proposed to help the implementation at application level.

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