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.
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Cui, X., Pullum, L.L., Treadwell, J., Patton, R.M., Potok, T.E. (2008). Particle Swarm Social Model for Group Social Learning in Adaptive Environment. In: Liu, H., Salerno, J.J., Young, M.J. (eds) Social Computing, Behavioral Modeling, and Prediction. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-77672-9_16
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DOI: https://doi.org/10.1007/978-0-387-77672-9_16
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