دانلود مقاله ISI انگلیسی شماره 43955
ترجمه فارسی عنوان مقاله

مدل سازی و توسعه یک سیستم برای مدیریت بحران، عامل گرا

عنوان انگلیسی
Agent-oriented modeling and development of a system for crisis management
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
43955 2013 13 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 40, Issue 16, 15 November 2013, Pages 6580–6592

ترجمه کلمات کلیدی
مهندسی نرم افزار عامل گرا - مدیریت بحران - الگوی تعامل - مدل رانده توسعه - سیستم چند عامله
کلمات کلیدی انگلیسی
Agent-oriented software engineering; Crisis management; Ingenias; Interaction pattern; Model-driven development; Multi-agent system
پیش نمایش مقاله
پیش نمایش مقاله  مدل سازی و توسعه یک سیستم برای مدیریت بحران، عامل گرا

چکیده انگلیسی

The management of crisis situations has been a challenging problem from different points of views, such as communication efficiency and avoiding casualties. This paper presents a novel approach that includes an interaction organization pattern for Multi-agent Systems (MASs) in crisis management, abstracted from several existing case studies in which the agents follow a sequence of interactions and the organization must optimize the use of human resources. The pattern considers an emergent organization of peers that adopt different roles according to the circumstances. The key features of the organization are its robustness, scalability (in terms of both agents and roles), flexibility to deal with a changing environment, and the efficient use of resources. In order to validate the organization, the paper presents its modeling and development with the Ingenias methodology, conforming the corresponding MAS. This development follows a model-driven approach, which allows a smooth transition from the specification to the code, and a low-cost testing of the system with different settings. Another key aspect is the application of metrics for validating and improving the MAS in terms of response time. The MAS has been tested with 600 agents representing 200 citizens, showing its performance.