context-dependent value of information and cost-sensitive learning for situational awareness on smart phones

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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 suc as user identity, location and time. As smart phones become increasingly outfitted with sensors, and have increasing ability to query the local environment for additional information, the problem of prioritization of information becomes important. This problem involves deciding which sensors to gather information from in order to best accommodate user needs and preferences in a given context. These decisions are important on smart phones, as they impact the amount of processing necessary, and thus the device cost and battery life. Our research approaches this problem in two ways. First, we apply existing sensor selection mechanisms, based soley on the value of information, to smart phone applications. These existing methods have well-studied solutions, but have not been applied specifically in this domain. Second, we study the open problem of sensor selection within decision-making processes, in which observational features are selected based on long-term impact on the decisions made by the smart. This second problem is more challenging, because information gathering can be useful for decisions made at some point in the future, and making predictions about the utility of information is an unsolved problem. For example, it may be worthwhile to gather pricing information for dinner items while the user is out for lunch, on a day when the user's schedule leaves little time for purchases at dinner time. Our research has established a mechanism for observation aggregation in Markov decision processes, and we plan to extend this work by using these aggregate observations as indications of potential utility of information. This research will impact a number of fields which use automated decision making based on large quantities of sensor data.