The Internet provides opportunity for knowledge sharing among people with similar interests (i.e., buddies). Common methods available for people to identify buddies for knowledge sharing include emails, mailing lists, chat rooms, electronic bulletin boards, and newsgroups. However, these manual buddy finding methods are time consuming and inefficient. In this thesis, we propose an agent-based buddy finding methodology based on a combination of case-based reasoning methodology and fuzzy logic technique. We performed two experiments to assess the effectiveness of our proposed methodology. The first experiment was comprised of a stock market portfolio knowledge sharing environment in which a conventional cluster analysis was used as a benchmark to assess the technical goodness of the proposed methodology in identifying the clusters of buddies. Statistical analysis showed that there was no significant ranking difference between conventional cluster analysis and the proposed buddy-finding methodology in identifying buddies. Cluster analysis requires centralized database to form buddies (clusters) with similar properties. The unique advantage of our proposed agent-based buddy finding methodology is that it can identify similar buddies in distributed as well as centralized database environments. A second experiment, in the context of sharing musical-knowledge among human subjects, was used to find out whether selection of the buddies by the proposed methodology is as good as those done by human subjects. The findings from this latter empirical test showed that the buddies found by agents are as good as the buddies found manually by humans.