A Learning Process Using SVMs for Multi-agents Decision Classification

• Published in: 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Vol. 3; pp. 583 - 586
• Main Authors: Xiao, Yanshan, Deng, Feiqi, Liu, Bo, Liu, Shouqiang, Luo, Dan, Liang, Guohua
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 09.12.2008; IEEE
• Series: ACM Conferences
• Subjects: Applied computing > Operations research > Decision analysis; Australia; Automation; Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Multi-agent systems; Computing methodologies > Machine learning > Learning paradigms > Supervised learning > Supervised learning by classification; Computing methodologies > Machine learning > Machine learning approaches > Classification and regression trees; Data mining; Information systems > Information systems applications > Decision support systems; Information technology; Intelligent agent; Management training; multi-agent support vector machine; Multiagent systems; Risk management; Support vector machine classification; Support vector machines; multi-agent support vector machine
• ISBN: 9780769534961; 0769534961
• DOI: 10.1109/WIIAT.2008.430

In order to resolve decision classification problem in multipleagents system, this paper first introduces the architecture ofmultiple agents system. It then proposes a support vector machinesbased assessment approach, which has the ability to learn the rulesform previous assessment results from domain experts. Finally, theexperiment are conducted on the artificially dataset to illustratehow the proposed works, and the results show the proposed method haseffective learning ability for decision classification problems.