Learning to acquire information
Name
1704.06131.pdf
Description
Accepted version
Size
585.49 KB
Format
Unknown
Checksum (MD5)
2252e6b7e67bfa60f30305a830beb22e
Author(s)
Pu, Yewen
Kaelbling, Leslie P
Solar Lezama, Armando
Date Issued
2017
Citation
Kaelbling, Leslie P. and Solar Lezama, Armando. 2017. "Learning to acquire information."
Version
Author's final manuscript
Abstract
We consider the problem of diagnosis where a set of simple observations are used to infer a potentially complex hidden hypothesis. Finding the optimal subset of observations is intractable in general, thus we focus on the problem of active diagnosis, where the agent selects the next most-informative observation based on the results of previous observations. We show that under the assumption of uniform observation entropy, one can build an implication model which directly predicts the outcome of the potential next observation conditioned on the results of past observations, and selects the observation with the maximum entropy. This approach enjoys reduced computation complexity by bypassing the complicated hypothesis space, and can be trained on observation data alone, learning how to query without knowledge of the hidden hypothesis.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://auai.org/uai2017/accepted.php
