  People with Alzheimer's disease and related dementias (ADRD) often show the
problem of repetitive questioning, which brings a great burden on persons with
ADRD (PwDs) and their caregivers. Conversational robots hold promise of coping
with this problem and hence alleviating the burdens on caregivers. In this
paper, we proposed a partially observable markov decision process (POMDP) model
for the PwD-robot interaction in the context of repetitive questioning, and
used Q-learning to learn an adaptive conversation strategy (i.e., rate of
follow-up question and difficulty of follow-up question) towards PwDs with
different cognitive capabilities and different engagement levels. The results
indicated that Q-learning was helpful for action selection for the robot. This
may be a useful step towards the application of conversational social robots to
cope with repetitive questioning in PwDs.
