Dynamic Sampling Allocation for Selecting a Good Enough Alternative
- Resource Type
- Conference
- Authors
- Zhang, Gongbo; Chen, Chun-Hung; Jia, Qing-Shan; Peng, Yijie
- Source
- 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Automation Science and Engineering (CASE), 2020 IEEE 16th International Conference on. :1319-1324 Aug, 2020
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Resource management
Dynamic scheduling
Bayes methods
Gaussian distribution
Computational modeling
Frequency modulation
Covariance matrices
good enough
ranking and selection
Bayesian
stochastic control
dynamic sampling and allocation
- Language
- ISSN
- 2161-8089
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.