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This paper focuses on some of the key intelligent techniques for conflict resolution in Multi-Agent Decision Support Systems., Comment: 5 pages, 1 table, Sixth International Conference on Intelligence Computing and Information Systems, Cairo, Egypt, 2013

Crisis response requires information intensive efforts utilized for reducing uncertainty, calculating and comparing costs and benefits, and managing resources in a fashion beyond those regularly available to handle routine problems. This paper presents an Artificial Immune Systems (AIS) metaphor for agent based modeling of crisis response operations. The presented model proposes integration of hybrid set of aspects (multi-agent systems, built-in defensive model of AIS, situation management, and intensity-based learning) for crisis response operations. In addition, the proposed response model is applied on the spread of pandemic influenza in Egypt as a case study., Comment: 12 pages, 5 figures, and 5 tables. Submitted to MATES2010: Eighth German Conference on Multi-Agents System Technologies, September 21, 2010-September 23, 2010 in Karlsruhe, Germany.

Pandemic influenza has great potential to cause large and rapid increases in deaths and serious illness. The objective of this paper is to develop an agent-based model to simulate the spread of pandemic influenza (novel H1N1) in Egypt. The proposed multi-agent model is based on the modeling of individuals' interactions in a space time context. The proposed model involves different types of parameters such as: social agent attributes, distribution of Egypt population, and patterns of agents' interactions. Analysis of modeling results leads to understanding the characteristics of the modeled pandemic, transmission patterns, and the conditions under which an outbreak might occur. In addition, the proposed model is used to measure the effectiveness of different control strategies to intervene the pandemic spread., Comment: 9 pages, 9 figure, 2 tables, sent to INFOS2010 conference (waiting for acceptance)

There exist huge problems in the current practice of crisis response operations. Response problems are projected as a combination of failure in communication, failure in technology, failure in methodology, failure of management, and finally failure of observation. In this paper we compare eight crisis response systems namely: DrillSim [2, 13], DEFACTO [12, 17], ALADDIN [1, 6], RoboCup Rescue [11, 15], FireGrid [3, 8, 18], WIPER [16], D-AESOP [4], and PLAN C [14]. Comparison results will disclose the cause of failure of current crisis response operations (the response gap). Based on comparison results; we provide recommendations for bridging this gap between response operations and systems., Comment: 6 pages, 1 figure, 2 tables, accepted at The 9th International Conference on Artificial Intelligence and Digital Communications

Crisis response is a critical area of research, with encouraging progress in the past view yeas. The aim of the research is to contribute to building future crisis environment where software agents, robots, responders, crisis managers, and crisis organizations interact to provide advice, protection and aid. This paper discusses the crisis response domain requirements, and provides analysis of five crisis response systems namely: DrillSim [2], DEFACTO [15], ALADDIN [1], RoboCup Rescue [18], and FireGrid [3]. Analysis of systems includes systems architecture and methodology. In addition, we identified features and limitations of systems based on crisis response domain requirements., Comment: 6 pages, 1 figure, accepted at Fourth International Conference on Intelligent Computing and Information Systems 2009

Crisis response poses many of the most difficult information technology in crisis management. It requires information and communication-intensive efforts, utilized for reducing uncertainty, calculating and comparing costs and benefits, and managing resources in a fashion beyond those regularly available to handle routine problems. In this paper, we explore the benefits of artificial intelligence technologies in crisis response. This paper discusses the role of artificial intelligence technologies; namely, robotics, ontology and semantic web, and multi-agent systems in crisis response., Comment: 6 pages, 5 figures, 1 table, accepted for MENDEL 2008 14th International Conference on Soft Computing, June 18-20, Brno, Czech Republic