Abstract
We describe a study of the use of decision-theoretic policies for optimally joining human and automated problem-solving efforts. We focus specifically on the challenge of determining when it is best to transfer callers from an automated dialog system to human receptionists. We demonstrate the sensitivities of transfer actions to both the inferred competency of the spoken-dialog models and the current sensed load on human receptionists. The policies draw upon probabilistic models constructed via machine learning from cases that were logged by a call routing service deployed at our organization. We describe the learning of models that predict outcomes and interaction times and show how these models can be used to generate expected-utility policies that identify when it is best to transfer callers to human operators. We explore the behavior of the policies with simulations constructed from real-world call data.
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See D’Agostino (2005) for a reflection from the business community about the failure to date of automated speech recognition systems to penetrate widely.
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Horvitz, E., Paek, T. Complementary computing: policies for transferring callers from dialog systems to human receptionists. User Model User-Adap Inter 17, 159–182 (2007). https://doi.org/10.1007/s11257-006-9026-1
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DOI: https://doi.org/10.1007/s11257-006-9026-1