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

智能手机上的上下文信息可以用于针对特定情况定制应用程序(例如，根据位置、汽油价格和交通提供定制的路线建议)。然而，典型上下文感知智能电话应用使用非常有限的上下文信息，例如用户身份、位置和时间。随着智能手机越来越多地配备传感器，并具有越来越强的查询本地环境以获取额外信息的能力，信息的优先顺序问题变得重要起来。这个问题涉及决定从哪些传感器收集信息，以便在给定的环境中最好地满足用户的需求和偏好。这些决定对智能手机很重要，因为它们会影响必要的处理量，从而影响设备成本和电池寿命。我们的研究从两个方面探讨了这个问题。首先，我们将现有的基于信息价值的传感器选择机制应用到智能手机应用中。这些现有的方法都有经过充分研究的解决方案，但还没有专门应用于这一领域。其次，我们研究了决策过程中传感器选择的公开问题，其中观测特征是根据对SMART决策的长期影响来选择的。第二个问题更具挑战性，因为信息收集可能对未来某个时候的决策有用，而预测信息的效用是一个悬而未决的问题。例如，在用户外出吃午餐时收集晚餐项目的价格信息可能是值得的，因为用户的日程安排在晚餐时间几乎没有时间购买。我们的研究已经建立了马尔可夫决策过程中的观测聚集机制，我们计划通过使用这些聚集的观测作为潜在的信息有用的指示来扩展这项工作。这项研究将对使用基于大量传感器数据的自动化决策的一些领域产生影响。

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.