Abstract
This report presents a study of integrating particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the social learning of self-organized groups and their collective searching behavior in an adaptive environment. The objective of this research is to apply the particle swarm metaphor as a model of social learning for a dynamic environment. The research provides a platform for understanding and insights into knowledge discovery and strategic search in human self-organized social groups, such as human communities.
| Original language | English |
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| Title of host publication | Social Computing, Behavioral Modeling, and Prediction, 2008 |
| Editors | John J. Salerno, Michael J. Young, Huan Liu |
| Publisher | Springer |
| Pages | 141-150 |
| Number of pages | 10 |
| ISBN (Print) | 9780387776712 |
| DOIs | |
| State | Published - 2008 |
| Event | 1st International workshop on Social Computing, Behavioral Modeling and Prediction, 2008 - Phoenix, United States Duration: Apr 1 2008 → Apr 2 2008 |
Publication series
| Name | Social Computing, Behavioral Modeling, and Prediction, 2008 |
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Conference
| Conference | 1st International workshop on Social Computing, Behavioral Modeling and Prediction, 2008 |
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| Country/Territory | United States |
| City | Phoenix |
| Period | 04/1/08 → 04/2/08 |
Funding
Acknowledgements Prepared by Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831-6285, managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725; and by Lockheed Martin, partially funded by internal Lockheed research funds.