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The abundance of DTV (digital television) programs precipitates a need for new tools to help people find interesting programs and personalize TV viewing experience. We describe the design, implementation, and evaluation of an agent based adaptive program personalization system, which is designed to assist users by adapting to their personal preferences. We provide the architecture of the system, details its components, and presents the inter agent communication mechanism for the multiagent system. In our system, the feature representation and similarity measurement is based on VSM (vector space model), while the preference knowledge learning algorithm is using relevance feedback. We describe the prototype implementation, which has been implemented on PC/Linux. Thirdly, it describes our experiments and performance evaluation on the prototype. The experiment results are encouraging, which shows that the system proposed is useful to TV consumers. Finally, we summarizes the conclusions, and ends with directions for future work.