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

arXiv: https://arxiv.org/abs/2104.08940 · HTML: https://mlsanity.com/paper/2104.08940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 47 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Deep Reinforcement Learning for Dialogue Generation](https://mlsanity.com/paper/1606.01541/) (2016)
- [SimpleDS: A Simple Deep Reinforcement Learning Dialogue System](https://mlsanity.com/paper/1601.04574/) (2016)
- [Proceedings of the Workshop on Social Robots in Therapy: Focusing on Autonomy and Ethical Challenges](https://mlsanity.com/paper/1812.07613/) (2018)

## Built on by (most cited first, 1 of 1)

- [Learning-Based Strategy Design for Robot-Assisted Reminiscence Therapy Based on a Developed Model for People with Dementia](https://mlsanity.com/paper/2109.02194/) (2021)

## Abstract (arXiv)

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.