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

A Simulated Experiment to Explore Robotic Dialogue Strategies for People with Dementia