Policies for Transferring Callers from Dialog Systems to Human Receptionists
Eric Horvitz and Tim Paek
Redmond, Washington 98052
Author email: firstname.lastname@example.org, email@example.com
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.
Keywords: Spoken dialog systems, machine learning, human-machine systems, probabilistic user modeling, complementary computing
In: E. Horvitz and T. Paek. Complementary Computing: Policies for Transferring Callers from Dialog Systems to Human Receptionists. User Modeling and User Adapted Interaction 17 (2007).
The original publication is available at www.springerlink.com.