Automatic Generation of a Multi Agent System for Crisis Management by a Model Driven Approach

Abstract

Considering the increasing occurrences of unexpected events and the need for pre-crisis planning in order to reduce risks and losses, modeling instant response environments is needed more than ever. Modeling may lead to more careful planning for crisis-response operations, such as team formation, task assignment, and doing the task by teams. A common challenge in this way is that the model should be understandable for crisis managers, such that they could exploit from the consequences of modeling. To run the model and view the results, the model should be converted to a program. The crisis manager would run the program to see how the model operations, including team formation, task allocation, and task performance, are done. In this paper, the executable code of a multi agent system is automatically generated from a model which is designed based on model driven approach. A domain specific modeling language named ERE-ML and its related tool are used, and some new features are added to this language. To evaluate conversion code output, the case study of Bam earthquake is implemented, and the scenarios defined in the system are visualized.

Keywords

References

[1] K. Mustapha, H. Mcheick, and S. Mellouli, “Modeling and simulation agent-based of natural disaster complex systems,” in Proceedings of the 4th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, pp. 148–155, 2013. [2] I. Thabet, M. Chaawa, and L. Ben Said, “A multi-agent organizational model for a snow storm crisis management,” in Proceedings of the International Conference on Information Systems for Crisis Response and Management in Mediterranean Countries, pp. 143–156, 2014. [3] J. M. Gascueña, E. Navarro, and A. Fernández-Caballero, “Model-driven engineering techniques for the development of multi-agent systems,” Engineering Applications of Artificial Intelligence, vol. 25, no. 1, pp. 159–173, 2012. [5] Y. S. Lopes, “Desenvolvimento orientado a modelos em sistemas multi-agentes com diferentes arquiteturas internas de agente.,” M.Sc. thesis, Ceará State University, 2012. [6] B. Bauer, J. P. Müller, and J. Odell, “Agent UML: A Formalism for Specifying Multiagent Interaction,” International Journal of Software Engineering and Knowledge Engineering, vol. 11, no. 3, pp. 207–230, 2001. [7] OMG, “Unified Modeling Language (UML) - Version 2.5.” [Online]. Available: http://www.omg.org/spec/UML/2.5/. [Accessed: 26-Mar-2017]. [8] M. Winikoff, “Towards making agent UML practical: A textual notation and a tool,” in Proceedings of the International Conference on Quality Software, pp. 401–406, 2005. [9] C. Hahn, “A Domain Specific Modeling Language for Multiagent Systems,” in Proceedings of 7th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2008), pp. 233–240, 2008. [10] R. F. Jorge J. Gómez-Sanz, “Agent Oriented Software Engineering with INGENIAS,” in Proceedings of the International Central and Eastern European Conference on Multi-Agent Systems, pp. 394–403, 2003. [11] J. Pavón, J. Gómez-Sanz, and R. Fuentes, “The INGENIAS Methodology and Tools,” in Agent-oriented methodologies., B. Henderson-Sellers and P. Giorgini, Ed. IGI Global, 2005, pp. 236–276. [12] J. J. Gómez-Sanz, C. R. Fernández, and J. Arroyo, “Model driven development and simulations with the INGENIAS agent framework,” Simulation Modelling Practice and Theory, vol. 18, no. 10, pp. 1468–1482, 2010. [13] V. T. Da Silva, R. Choren, and C. J. P. De Lucena, “MAS-ML: a multiagent system modelling language,” International Journal of Agent-Oriented Software Engineering, vol. 2, no. 4, pp. 382–421, 2008. [14] V. Silva, A. Garcia, A. Brandao, C. Chavez, C. Lucena, and P. Alencar, “Taming agents and objects in software engineering,” Software Engineering for Large-scale Multi-agent Systems, pp. 1–28, 2003. [15] E. J. T. Gonçalves, K. Farias, M. I. Cortés, A. R. Feijó, F. R. Oliveira, and V. T. Da Silva, “MAS-ML tool: A modeling environment for multi-agent systems,” in Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS 2011), pp. 192–197, 2011. [16] Y. S. Lopes, E. J. T. Goncalves, M. I. Cortés, and E. S. S. Freire, “A MDA Approach Using MAS-ML 2.0 and JAMDER,” in 13th International Workshop on Agent-Oriented Software Engineering, 2012. [17] S. H. Othman, G. Beydoun, and V. Sugumaran, “Development and validation of a disaster management metamodel,” Information Processing and Management, vol. 50, pp. 235–271, 2014. [18] S. H. Othman and G. Beydoun, “A Metamodel-based Knowledge Sharing System for Disaster Management,” Expert Systems with Applications, vol. 63, pp. 49–65, 2016. [19] H. Betke, “Structure and Elements of Disaster Response Processes – A General Meta-Model,” in Proceedings of the Information Systems for Crisis Response and Management Conference (ISCRAM 2015), pp. 1-5, 2015. [20] S. H. Othman and G. Beydoun, “Metamodelling Approach To Support Disaster Management Knowledge Sharing,” in 21st Australasian Conference on Information Systems (ACIS 2010), pp. 1–10, 2010. [21] T. Adamzadeh, B. Zamani, and A. Fatemi, “A modeling language to model mitigation in emergency response environments,” in Proceedings of the 4th International Conference on Computer and Knowledge Engineering (ICCKE 2014), pp. 302–307, 2014.
Soft Computing Journal
Volume 6, Issue 2 - Serial Number 12
September 2017
Pages 2-17

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History

  • Receive Date: 23 May 2021
  • Revise Date: 03 July 2024
  • Accept Date: 23 May 2021

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