Look versus Leap: Computing Value of Information with High-Dimensional Streaming Evidence
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2561-2566.
Abstract
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.
@inproceedings{ijcai2013p377,
title = {Look versus Leap: Computing Value of Information with High-Dimensional Streaming Evidence},
author = {Rosenthal, Stephanie and Bohus, Dan and Kamar, Ece and Horvitz, Eric},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {2561--2566},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/377},
}
TY - CPAPER TI - Look versus Leap: Computing Value of Information with High-Dimensional Streaming Evidence AU - Rosenthal, Stephanie AU - Bohus, Dan AU - Kamar, Ece AU - Horvitz, Eric T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 2561 EP - 2566 UR - https://ijcai.org/proceedings/2013/377 ER -Copied
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