Name
Abstract | ||
|---|---|---|
We consider the problem of selecting a good enough alternative from a finite set of alternatives. Instead of selecting the exactly best alternative, our work aims to maximize the probability of correctly selecting an alternative in an acceptable subset. Under a Bayesian framework, we formulate the problem as a stochastic control problem. We propose a dynamic allocation scheme for selecting a good enough alternative, which optimizes a value function approximation one-step ahead. Numerical results demonstrate the proposed sampling procedure is more efficient than other sampling allocation methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CASE48305.2020.9216838 | 2020 IEEE 16TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE) |
Keywords | DocType | ISSN |
good enough, ranking and selection, Bayesian, stochastic control, dynamic sampling and allocation | Conference | 2161-8070 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gongbo Zhang | 1 | 0 | 0.34 |
| Chun-Hung Chen | 2 | 1095 | 117.31 |
| Qing-Shan Jia | 3 | 442 | 51.24 |
| Yijie Peng | 4 | 32 | 12.59 |