Risk-Averse Biased Human Policies in Assistive Multi-Armed Bandit Settings
This addresses human-robot team efficiency in assistive scenarios, but it is incremental as it builds on existing bandit and bias models.
The paper tackles the problem of human risk-aversion in assistive multi-armed bandit settings by expanding the model to use observable rewards, resulting in an algorithm that increases team utility by eliminating bias and enabling more rational choices.
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.