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In this paper, we present a technique for learning optimal dialog management strategies. An automatic dialog generation technique, including a simulation of the communication channel, has been developed to acquire the required data, train dialog models, and explore new dialog strategies in order to learn the optimal one. A set of quantitative and qualitative measures has been defined to evaluate the quality of the strategies learned. We provide empirical evidence of the benefits of our proposal through its application to explore the space of possible dialog strategies for the UAH spoken dialog system.