Boosting-based learning agents for experience classification

Po Chun Chen, Xiaocong Fan, Shizhuo Zhu, John Yen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The capability of learning from experience is of critical importance in developing multi-agent systems supporting dynamic group decision making. In this paper, we introduce a hierarchical learning approach, aiming to support hierarchical group decision making where the decision makers at lower levels only have partial view of the whole picture. To further understand such a hierarchical learning concept, we implemented a learning component within the R-CAST agent architecture, with lower-level learners using the LogitBoost algorithm with decision stumps. The boosting-based learning agents were then used in our experiments to classify experience instances. The results indicate that hierarchical learning can largely improve decision accuracy when lower-level decision makers only have limited information accessibility.

Original languageEnglish (US)
Title of host publicationProceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06
PublisherIEEE Computer Society
Pages385-388
Number of pages4
ISBN (Print)9780769527482
DOIs
StatePublished - Jan 1 2006
Event2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT'06 - Hong Kong, China
Duration: Dec 18 2006Dec 22 2006

Publication series

NameProceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06

Other

Other2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT'06
CountryChina
CityHong Kong
Period12/18/0612/22/06

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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