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

• URL: http://arxiv.org/abs/2104.08940v1
• Date: Sun, 18 Apr 2021 19:35:19 GMT
• Title: A Simulated Experiment to Explore Robotic Dialogue Strategies for People with Dementia
• Authors: Fengpei Yuan, Amir Sadovnik, Ran Zhang, Devin Casenhiser, Eun Jin Paek, Si On Yoon, and Xiaopeng Zhao
• Abstract summary: We propose a partially observable markov decision process (POMDP) model for the PwD-robot interaction in the context of repetitive questioning. We used Q-learning to learn an adaptive conversation strategy towards PwDs with different cognitive capabilities and different engagement levels. This may be a useful step towards the application of conversational social robots to cope with repetitive questioning in PwDs.
• Score: 2.5412519393131974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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