Prompt selection with reinforcement learning in an AT&t call routing application
نویسندگان
چکیده
Reinforcement Learning (RL) algorithms provide a type of unsupervised learning that is especially well suited for the challenges of spoken dialogue systems (SDS) design. SDS are constantly subjected to new environments in the form of new groups of users, and RL provides an approach for automated learning that can adapt to new environments without costly supervision. In this paper, we describe some results from experiments with RL to select prompts for a call routing application. A simulation of the dialogue outcomes were used to experiment with different scenarios and demonstrate how RL can make a system more robust without supervision or developer intervention.
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Dialogue Strategy Optimization with Reinforcement Learning in an AT&T Call Routing Application
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