Eric Horvitz, Paul Koch, Raman Sarin, Johnson Apacible, and Muru Subramani
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Inference and decision making with probabilistic user models may be infeasible on portable devices such as cell phones. We highlight the opportunity for storing and using precomputed inferences about ideal actions for future situations, based on offline learning and reasoning with the user models. As a motivating example, we focus on the use precomputation of call-handling policies for cell phones. The methods hinge on the learning of Bayesian user models for predicting whether users will attend meetings on their calendar and the cost of being interrupted by incoming calls should a meeting be attended.
Keywords: Decision-theoretic models of communication, precomputation of user models, value of information, intelligent communication policies
In: E. Horvitz, P. Koch, R. Sarin, J. Apacible, and M. Subramani (2005). Bayesphone: Precomputation of Context-Sensitive Policies for Inquiry and Action in Mobile Devices, User Modeling 2005, Edinburgh, Scotland, July 2005.
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