Learning cost-sensitive active classifiers
Artificial Intelligence
Decision-theoretic active sensing for autonomous agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Test-Cost Sensitive Naive Bayes Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
VOILA: efficient feature-value acquisition for classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Optimal value of information in graphical models
Journal of Artificial Intelligence Research
Solving POMDPs with continuous or large discrete observation spaces
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers
AmI '09 Proceedings of the European Conference on Ambient Intelligence
Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs
Artificial Intelligence
Paradoxes in Learning and the Marginal Value of Information
Decision Analysis
Rapid specification and automated generation of prompting systems to assist people with dementia
Pervasive and Mobile Computing
Resource-Bounded information extraction: acquiring missing feature values on demand
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Workshop overview for the international workshop on situation, activity and goal awareness
Proceedings of the 13th international conference on Ubiquitous computing
People, sensors, decisions: Customizable and adaptive technologies for assistance in healthcare
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
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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 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 paper, we present a model for 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 information gathering can be useful for decisions made at some point in the future. This paper 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.