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Expert discovery and knowledge mining in complex multi-agent systems

Citation

Zhang, M and Tang, X and Gu, J, Expert discovery and knowledge mining in complex multi-agent systems, Journal of Systems Science and Systems Engineering, 16, (2) pp. 222-234. ISSN 1004-3756 (2007) [Refereed Article]


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Copyright Statement

Copyright 2007 Systems Engineering Society of China & Springer-Verlag. This is a post-peer-review, pre-copyedit version of an article published in Journal of Systems Science and Systems Engineering. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11518-007-5043-9

DOI: doi:10.1007/s11518-007-5043-9

Abstract

Complex problem solving requires diverse expertise and multiple techniques. In order to solve such problems, complex multi-agent systems that include both of human experts and autonomous agents are required in many application domains. Most complex multi-agent systems work in open domains and include various heterogeneous agents. Due to the heterogeneity of agents and dynamic features of working environments, expertise and capabilities of agents might not be well estimated and presented in these systems. Therefore, how to discover useful knowledge from human and autonomous experts, make more accurate estimation for expertsí capabilities and find out suitable expert(s) to solve incoming problems ("Expert Mining") are important research issues in the area of multi-agent system. In this paper, we introduce an ontology-based approach for knowledge and expert mining in hybrid multi-agent systems. In this research, ontologies are hired to describe knowledge of the system. Knowledge and expert mining processes are executed as the system handles incoming problems. In this approach, we embed more self-learning and self-adjusting abilities in multi-agent systems, so as to help in discovering knowledge of heterogeneous experts of multi-agent systems.

Item Details

Item Type:Refereed Article
Keywords:multi-agent systems, knowledge discovery
Research Division:Information and Computing Sciences
Research Group:Computer vision and multimedia computation
Research Field:Pattern recognition
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Application software packages
ID Code:140740
Year Published:2007
Web of Science® Times Cited:3
Deposited By:Information and Communication Technology
Deposited On:2020-09-02
Last Modified:2020-10-23
Downloads:1 View Download Statistics

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