  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.
