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An Evolutionary Framework for Multi-Agent Organizations


Authors: B. Li, H. Yu, Z. Shen, L. Cui, and V. R. Lesser
Title: An Evolutionary Framework for Multi-Agent Organizations
Abstract: The organizational design of a multi-agent system (MAS) is important for its efficiency, adaptability and robustness. However, finding suitable organizational structures for different MASs is a challenging problem. In this paper, we propose a Framework of Evolutionary Optimization for Agent Organizations (FEVOR) based on Genetic Programming for optimizing tree-structured MASs. FEVOR employs a flexible representation of organizations and may be applied to a wide range of organizational forms such as pure hierarchies, holarchies, and federations. Compared to existing work, FEVOR is capable of efficient quantitative search and less vulnerable to stalling at local optima due to its non-greedy nature. Extensive experiments for optimizing an information retrieval system have been conducted to demonstrate the advantages of FEVOR in generating suitable MAS organizations for adaptive environments.
Keywords: Agent organization; Evolutionary computing
Conference Name: 2015 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT'15)
Location: Singapore, Singapore
Publisher: IEEE
Year: 2015
Accepted PDF File: An_Evolutionary_Framework_for_Multi-Agent_Organizations_accepted.pdf
Permanent Link: http://dx.doi.org/10.1109/WI-IAT.2015.45
Reference: B. Li, H. Yu, Z. Shen, L. Cui, and V. R. Lesser, “An evolutionary framework for multi-agent organizations,” in Proceedings of the 2015 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT’15). IEEE, December 2015, accepted for publication.
bibtex: 
@inproceedings{LILY-c54, 
    author	= {Li, Boyang and Yu, Han and Shen, Zhiqi and Cui, Lizhen and Lesser, Victor R.},
    title	= {An Evolutionary Framework for Multi-Agent Organizations},  
    booktitle	= {Proceedings of the 2015 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT'15)}, 
    year		= {2015}, 
    month	= {December}, 
    pages	= {35-38}, 
    location	= {Singapore, Singapore},
    publisher	= {IEEE},
 }