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We are building an adaptive driver support system using an agent architecture and machine learning techniques. The goal of the system is to help the drivers have a safer, more enjoyable and more productive driving experiences, by managing their attention and workload. In this paper, we describe the overall architecture of the driver support system and how we apply machine learning techniques to have the system adapt to the driving behavior of each individual driver. The architecture has been partially implemented in a prototype system built upon a high-fidelity driving simulator, allowing us to run experimental tests on the interaction between the system and human users. Once the system demonstrates the desired capabilities, it will be tested in a real car in an actual driving environment.