## 3 Question-Answer-Response Dialogue Model
### Utterance Selection in Each State
This graph shows that some of the participants were talking for more than 5 min (i.e., Participants 2, 15, 18, 19, and 20). Especially, there were three participants with severe dementia in the two-robot scenario. Although we cannot conclude with certainty based on such small data, this result appears to provide a new hypothesis that some elderly people with severe dementia may actively speak when placed in a two-robot scenario. Further research are required to investigate whether using multiple robots is better for elderly people with severe dementia. ### Influence of Topics on Participants' Ulterance Time

In order to investigate whether the topic of the questions influence the verbal participation time of the participants, we calculated the mean of the utterance time of the participants in each topic. The results were as follows: In the one-robot scenario, the utterance time of the topic of travel, health, and childhood were 11.9 s (SD = 21.3), 6.8 s (SD = 5.1), and 14.7 s (SD = 27.4). In the two-robot scenario, the utterance time of the topic of travel, health, and childhood were 9.3 s (SD = 10.5), 9.2 s (SD = 11.5), and 12.0 s (SD = 12.7). We analyzed the results using two-way mixed ANOVA, which

Figure 12: Averages of the scores of the question to the caregivers, grouped by dementia levels of the participants. Figure 13: Utterance time of each participant grouped by dementia levels. has scenario factor and topic factor. The results showed that there was no main effect in the scenario factor (F(1,20) = 0.01, \(p\) = 0.920), no main effect in the topic factor (F(2,40) = 2.094, \(p\) = 0.136), and no interaction between the two factors (F(2,40) = 0.933, \(p\) = 0.402). Therefore, it is unlikely that differences in topics had a systematic effect on utterance time. Because the variance of the utterance time is very large, the influence of the topic of the question on the utterance time appeared to be considerably dependent on the individual. As an interesting example, we found that a topic stimulated participants' memory and the participants began to talk about their life for a long time. Specifically, participant 2 spent about 2 min talking about her initial visit to the nursing home when the robot asked, \"Have you ever had a honeymoon?\". Moreover, in response to the question, \"What class did you like in elementary school?\" she had talked about her childhood struggles for about 3 min. Participant 18 spent about 3 min talking about their experiences during World War II when asked \"Have you lived around here since you were a child?\". Their utterance times were quite long considering that usual answers of other participants were only one or two sentences. More surprisingly, even the caregivers, who have been interacting with the participants in their daily lives, did not know the stories of the participants until that time. These examples are interesting from the aspect of robots being potentially able to elicit a much deeper story from the elderly if the robot chooses topics adjusted to the individual. ### Pros and Cons of Our System

We found several pros and cons of the twin-robot dialogue system through the field trial. * Leading a dialogue by a robot **Pros**. Participants who have no topic to discuss might have easily taken part in a dialogue. In general, it is quite challenging for people to initiate a dialogue unless they have topics they would want to talk about. We found that many participants had no topic to discuss with the robot. By the robot initiating a dialogue, those participants could have participated in the dialogue without worrying about initiating it. **Cons**. Dialogue initiation by the robot may have frustrated some participants in case they had something they would have preferred to talk about with the robot. * Patterning a dialogue **Pros**. Participants who are not good at communicating smoothly might have easily followed a dialogue because the user could have predicted the flow of the dialogue.