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

TL;DR

This paper develops a POMDP-based model and uses Q-learning to optimize robot dialogue strategies, aiming to reduce repetitive questioning in people with dementia and ease caregiver burden.

Contribution

It introduces a novel POMDP framework combined with Q-learning for adaptive robot dialogue strategies tailored to dementia patients.

Findings

Q-learning improved robot action selection.

Adaptive strategies tailored to cognitive levels.

Potential to alleviate caregiver burden.

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.