2012 Nov 29 at 09:00
DC 2306C (AI Lab)
Veronika Koltunova, graduate student, David R. Cheriton School of Comp. Sci., Univ. Waterloo
Context information on a smart phone can be used to tailor applications for specific situations (e.g. provide tailored routing advice based on location, gas prices and traffic). However, typical context- aware smart phone applications use very limited context information such as user identity, location and time. In the future, smart phones will need to decide from a wide range of sensors to gather information from in order to best accommodate user needs and preferences in a given context. In this thesis, we present a model for active sensor selection within decision-making processes, in which observational features are selected based on longer-term impact on the decisions made by the smart phone. This problem is more challenging than measuring information gain, because context information gathering can improve the quality of the decisions made at some point in the future. This thesis formulates the problem as a partially observable Markov decision process (POMDP), and shows how the POMDP can integrate partially available sensor information with user goals and preferences, allowing a seamless fusion of uncertain sensor data with complex and long-term decision-making on the mobile device.