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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 experts’ 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 |
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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: | 12 View Download Statistics |
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