Abstract
We consider a problem of active diagnosis, where the goal is to efficiently identify an unknown object by sequentially selecting, and observing, the responses to binary valued queries. We assume that query observations are noisy, and further that the noise is persistent, meaning that repeating a query does not change the response. Previous work in this area either assumed the knowledge of the query noise distribution, or that the noise level is sufficiently low so that the unknown object can be identified with high accuracy. We make no such assumptions, and introduce an algorithm that returns a ranked list of objects, such that the expected rank of the true object is optimized. Furthermore, our algorithm does not require knowledge of the query noise distribution.
Original language | English (US) |
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Pages (from-to) | 155-163 |
Number of pages | 9 |
Journal | Journal of Machine Learning Research |
Volume | 15 |
State | Published - 2011 |
Event | 14th International Conference on Artificial Intelligence and Statistics, AISTATS 2011 - Fort Lauderdale, FL, United States Duration: Apr 11 2011 → Apr 13 2011 |
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Control and Systems Engineering
- Statistics and Probability