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A trainable model of the human operator in information acquisition tasks is described. The purpose of this model, called the adaptive information selector (AIS), is to select and present textual messages automatically to users in computer-based tactical systems. AIS algorithms, which control and present messages in order of priority in real time, are based on multiattribute characterization of tactical messages. The AIS employs an adaptive pattern recognition model to "learn" user preference structure incrementally during actual task performance. Across each of the command situations, the priority of messages is determined by the AIS in accord with the information selection behavior exhibited by the user in the model-training mode. The AIS was implemented and tested in a simulated environment. The results demonstrated the model's capability to 1) converge on distinctive information processing strategies exhibited by different operators; and 2) effectively present messages in their order of priority for each operator. The AIS is potentially useful in performing information distribution functions in command and control systems and in aiding the performance of personalized searches of large data bases.