Learning to Acquire Information

04/20/2017
by   Yewen Pu, et al.
0

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.

READ FULL TEXT
research
12/08/2016

Prediction with a Short Memory

We consider the problem of predicting the next observation given a seque...
research
07/04/2012

Efficient Test Selection in Active Diagnosis via Entropy Approximation

We consider the problem of diagnosing faults in a system represented by ...
research
01/09/2020

Deep Learning Enabled Uncorrelated Space Observation Association

Uncorrelated optical space observation association represents a classic ...
research
02/18/2023

Maximum Entropy Estimator for Hidden Markov Models: Reduction to Dimension 2

In the paper, we introduce the maximum entropy estimator based on 2-dime...
research
05/17/2023

The Principle of Uncertain Maximum Entropy

The principle of maximum entropy, as introduced by Jaynes in information...
research
11/06/2020

Learning Online Data Association

When an agent interacts with a complex environment, it receives a stream...
research
06/27/2019

Emergence of Exploratory Look-Around Behaviors through Active Observation Completion

Standard computer vision systems assume access to intelligently captured...

Please sign up or login with your details

Forgot password? Click here to reset