arXiv
Open Access
2021
Risk-Averse Biased Human Policies in Assistive Multi-Armed Bandit Settings
Michael Koller
Timothy Patten
Markus Vincze
Abstrak
Assistive multi-armed bandit problems can be used to model team situations between a human and an autonomous system like a domestic service robot. To account for human biases such as the risk-aversion described in the Cumulative Prospect Theory, the setting is expanded to using observable rewards. When robots leverage knowledge about the risk-averse human model they eliminate the bias and make more rational choices. We present an algorithm that increases the utility value of such human-robot teams. A brief evaluation indicates that arbitrary reward functions can be handled.
Topik & Kata Kunci
Penulis (3)
M
Michael Koller
T
Timothy Patten
M
Markus Vincze
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2021
- Bahasa
- en
- Sumber Database
- arXiv
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- Open Access ✓