Principles and applications of continual computation
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
An Approximate Nonmyopic Computation for Value of Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 5th international conference on Multimodal interfaces
BusyBody: creating and fielding personalized models of the cost of interruption
CSCW '04 Proceedings of the 2004 ACM conference on Computer supported cooperative work
Myopic Policies in Sequential Classification
IEEE Transactions on Computers
Efficient non-myopic value-of-information computation for influence diagrams
International Journal of Approximate Reasoning
Near-optimal observation selection using submodular functions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Explorations in engagement for humans and robots
Artificial Intelligence
Greedy algorithms for sequential sensing decisions
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Models for multiparty engagement in open-world dialog
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Challenge problems for artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Coordinate: probabilistic forecasting of presence and availability
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Combining human and machine intelligence in large-scale crowdsourcing
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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A key decision facing autonomous systems with access to streams of sensory data is whether to act based on current evidence or to wait for additional information that might enhance the utility of taking an action. Computing the value of information is particularly difficult with streaming high-dimensional sensory evidence. We describe a belief projection approach to reasoning about information value in these settings, using models for inferring future beliefs over states given streaming evidence. These belief projection models can be learned from data or constructed via direct assessment of parameters and they fit naturally in modular, hierarchical state inference architectures. We describe principles of using belief projection and present results drawn from an implementation of the methodology within a conversational system.