A Simulated Experiment to Explore Robotic Dialogue Strategies for People with Dementia
This addresses the burden on caregivers of people with dementia, but it is incremental as it builds on existing methods in a specific domain.
The paper tackled the problem of repetitive questioning in people with dementia by proposing a POMDP model and using Q-learning to learn adaptive conversation strategies for robots, with results indicating Q-learning was helpful for action selection.
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.